We are launching a strategic pool of AI specialists to accelerate the growth of our AI Practice and turn strong market demand into scalable, repeatable capabilities. This role goes beyond project delivery-it is about shaping how AI is built, shared, and scaled across the company.

As part of this group, you will play a key role in internal capability development, with a strong focus on designing and evolving AI Accelerators-reusable frameworks, templates, and reference architectures covering GenAI, LLM-based solutions, ML pipelines, and cloud-native AI systems. These accelerators will become the backbone of our AI offerings, enabling faster delivery, higher quality, and consistent technical standards across projects.

A second core dimension of the role is knowledge leadership. Strategic hires will actively contribute to internal knowledge-sharing initiatives: technical deep dives, hands-on workshops, demos, and best-practice sessions covering topics such as GenAI orchestration, RAG, model deployment, prompt and system design, observability, and cost-efficient AI architectures. You will help raise the overall AI maturity of the organization while mentoring engineers and influencing technical direction. In addition, members of this group will act as AI project jumpstarters, joining new client engagements for up to 50% allocation over 1-2 months. In this capacity, you will help define solution architectures, validate technical approaches, build initial prototypes, and set strong engineering foundations before teams scale. This gives you exposure to diverse domains and cutting-edge AI use cases while maximizing your impact across multiple initiatives.

Project Description

We are looking for a highly skilled and innovative Machine Learning Engineer to develop and expand our Generative AI and Computer Vision capabilities. This role involves accelerating Technical Sales Support, Architecture and PoC development.

Technologies

• Python
• Git, CI/CD practices for ML and data pipelines
• AWS and/or Azure
• PyTorch, TensorFlow
• MLflow
• LLMs and NLP tasks
• Generative AI concepts
• LangChain/LangGraph, LlamaIndex
• Qdrant, Pinecone, Weaviate, FAISS or similar
• RNNs, Transformers
• Dataset labeling, annotation tools (e.g., Label Studio, CVAT)
• Prometheus, Grafana, DataDog, OpenTelemetry
• YOLO, SDD
• Segmentation models
• Image Classification

What You'll Do

• Lead the design, development, and deployment of scalable ML services;
• Architect and implement AI/ML solutions, ensuring model quality, governance, and security;
• Optimize ML workflows for performance and cost efficiency on cloud platforms (AWS, Azure, or GCP);
• Collaborate with data scientists, analysts, and business stakeholders to understand ML requirements;
• Mentor and guide junior engineers, promoting best practices in ML engineering;
• Develop and maintain documentation, ML models, and technical standards;
• Monitor, troubleshoot, and resolve issues in production for ML pipelines and environments;
• Stay up-to-date with emerging trends and technologies in AI/ML field;

Job Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field;
• 5+ years of experience in ML engineering, with at least 1 years of hands-on experience with LLMs;
• Strong proficiency in Python;
• Experience with cloud data platforms (AWS and/or Azure);
• Familiarity with CI/CD, DevOps, and infrastructure-as-code practices for ML engineering;
• Experience with data governance, security, and compliance in cloud environments;
• Excellent problem-solving, communication, and leadership skills;
• English: Upper Intermediate level or higher;

What Do We Offer

The global benefits package includes:

• Technical and non-technical training for professional and personal growth;
• Internal conferences and meetups to learn from industry experts;
• Support and mentorship from an experienced employee to help you professional grow and development;
• Health insurance;
• English courses;
• Sports activities to promote a healthy lifestyle;
• Flexible work options, including remote and hybrid opportunities;
• Referral program for bringing in new talent;
• Work anniversary program and additional vacation days.

Please complete the attached Internal Transfer Request Form and submit.  

Please make sure to apply with your Coupang e-mail address.  

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce.

We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.

Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.

Role Overview

As our Staff Machine Learning Engineer for Coupang Media Group, Ads Quality, you will be responsible for developing, designing machine learning models, optimization algorithms and new product features for our advertising platform. 

The Coupang Media Group (CMG) is responsible for providing vendors that sell their products on Coupang a host of marketing products and services.  We launched Product Ads performance advertising marketplace late in 2018.  We have grown rapidly to over 10,000 active advertisers and 150M daily ad impressions in only 5 months’ time. Our group is responsible for advertising brands and products inside our e-commerce website. To get an idea of how valuable that is, both Alibaba and Amazon make more money on advertising than on selling products.  

This is an opportunity to be part of building up a new development organization with strong revenue potential inside a successful company. We’re building full-stack engineering teams to handle large scale advertising problems, including real-time behavioral targeting, auctioning and bidding systems and search based advertising. 

We are in the growth phase of the organization, so you’ll be a part of managing the transition from growth phase to enterprise processes. 

In this role you will be responsible for innovating and building our new ad quality stack for our ads platform  and eventually extend it into a full ad exchange, DSP, and SSP. We currently handle about 1bn impressions per day from internal traffic alone. 

What You Will do

• Design features and build large-scale machine learning models and systems to improve ad targeting, relevance, ranking, and engagement 
• Design and implement large-scale ML systems for search ranking, semantic retrieval, query understanding, and personalized product discovery using state-of-the-art techniques such as transformer-based models, contrastive learning, and vector search
• Drive innovation in search relevance and user intent modeling using large language models (LLMs), embedding-based retrieval, and multi-modal learning
• Build and optimize ML pipelines using tools such as Apache Spark, Airflow, Kubeflow, and MLflow, ensuring reproducibility, scalability, and operational excellence
• Define and track key performance metrics to evaluate model impact and identify high[1]leverage opportunities for improvement
• Collaborate cross-functionally with product, engineering, and data science teams to align technical solutions with business goals and customer experience
• Mentor and grow engineering talent, fostering a culture of technical excellence, experimentation and continuous learning

Basic Qualifications

• Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related fields
• 4 years of professional experience in applied machine learning
• Experience in machine learning, deep learning, and statistical modeling
• Proficiency in Python and/or Java, with experience in building production grade ML systems

Preferred Qualifications

• Master’s or PhD in relevant technical fields
• Experience with search systems, information retrieval or recommendation engines
• Experience with LLMs, embeddings, and vector search technologies
• Experience with cloud platforms such as AWS, Google Cloud Platform including services like Vertex AI, BigQuery or SageMaker
• Experience working in startup or high-growth environments
• Proven ability to lead-cross-functional teams and deliver results in a multicultural, global organization
• Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, Scikit[1]learn, Keras, XGBoost, LightGMB, and H2o.ai
• Experience with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or Amazon SageMaker
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
• Demonstrated ability to work independently and manage ambiguity in fast-paced environments

General Description of All Benefits

• Annual bonus is 0-20% of the base salary
• Medical/Dental/Vision/Life, AD&D insurance
• Flexible Spending Accounts (FSA) & Health Savings Account (HSA)
• Long-term/Short-term Disability
• Employee Assistance Program (EAP) program
• 401K Plan with Company Match
• 18-21 days of the Paid Time Off (PTO) a year based on the tenure
• 12 Public Holidays
• 6 weeks Paid Parental leave
• Pre-tax commuter benefits
• MTV - [Free] Electric Car Charging Station

General Description of Other Compensation

“Other Compensation” includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale.

Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair styles), color, religion, religious creed (including religious dress and grooming practices), sex or gender (including pregnancy, childbirth, breastfeeding, and medical conditions related to pregnancy, childbirth or breastfeeding), gender identity, gender expression, sexual orientation, ,ancestry, national origin (including language use restrictions), age (40 and over), physical or mental disability, medical condition, genetic information, HIV/AIDS or Hepatitis C status, family status (including but not limited to marital or domestic partnership status), military or veteran status, use of a trained dog guide or service animal, political activities or affiliations, ancestry, citizenship, family and medical leave status, status as a victim of any violent crime, or any other characteristic or class protected by the laws or regulations in the locations where we operate.

Recruitment Process and Others

Recruitment Process

• Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer

• The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.

• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.

Details to Consider

• This job posting may be closed prior to the stated end date for application if all openings are filled.

• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.

• Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws.

Privacy Notice Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/

If you need assistance and/or a reasonable accommodation in the application of recruiting process due to a disability, please contact us at usrecruiting@coupang.com.

Requisition ID: R0072711

Please complete the attached Internal Transfer Request Form and submit.  

Please make sure to apply with your Coupang e-mail address.  

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce.

We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.

Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.

Role Overview

As our Staff Machine Learning Engineer for Coupang Media Group, Ads Quality, you will be responsible for developing, designing machine learning models, optimization algorithms and new product features for our advertising platform. 

The Coupang Media Group (CMG) is responsible for providing vendors that sell their products on Coupang a host of marketing products and services.  We launched Product Ads performance advertising marketplace late in 2018.  We have grown rapidly to over 10,000 active advertisers and 150M daily ad impressions in only 5 months’ time. Our group is responsible for advertising brands and products inside our e-commerce website. To get an idea of how valuable that is, both Alibaba and Amazon make more money on advertising than on selling products.  

This is an opportunity to be part of building up a new development organization with strong revenue potential inside a successful company. We’re building full-stack engineering teams to handle large scale advertising problems, including real-time behavioral targeting, auctioning and bidding systems and search based advertising. 

We are in the growth phase of the organization, so you’ll be a part of managing the transition from growth phase to enterprise processes. 

In this role you will be responsible for innovating and building our new ad quality stack for our ads platform  and eventually extend it into a full ad exchange, DSP, and SSP. We currently handle about 1bn impressions per day from internal traffic alone. 

What You Will do

• Design features and build large-scale machine learning models and systems to improve ad targeting, relevance, ranking, and engagement 
• Design and implement large-scale ML systems for search ranking, semantic retrieval, query understanding, and personalized product discovery using state-of-the-art techniques such as transformer-based models, contrastive learning, and vector search
• Drive innovation in search relevance and user intent modeling using large language models (LLMs), embedding-based retrieval, and multi-modal learning
• Build and optimize ML pipelines using tools such as Apache Spark, Airflow, Kubeflow, and MLflow, ensuring reproducibility, scalability, and operational excellence
• Define and track key performance metrics to evaluate model impact and identify high[1]leverage opportunities for improvement
• Collaborate cross-functionally with product, engineering, and data science teams to align technical solutions with business goals and customer experience
• Mentor and grow engineering talent, fostering a culture of technical excellence, experimentation and continuous learning

Basic Qualifications

• Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related fields
• 4 years of professional experience in applied machine learning
• Experience in machine learning, deep learning, and statistical modeling
• Proficiency in Python and/or Java, with experience in building production grade ML systems

Preferred Qualifications

• Master’s or PhD in relevant technical fields
• Experience with search systems, information retrieval or recommendation engines
• Experience with LLMs, embeddings, and vector search technologies
• Experience with cloud platforms such as AWS, Google Cloud Platform including services like Vertex AI, BigQuery or SageMaker
• Experience working in startup or high-growth environments
• Proven ability to lead-cross-functional teams and deliver results in a multicultural, global organization
• Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, Scikit[1]learn, Keras, XGBoost, LightGMB, and H2o.ai
• Experience with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or Amazon SageMaker
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
• Demonstrated ability to work independently and manage ambiguity in fast-paced environments

Pay & Benefits

Our compensation reflects the cost of living across several US geographic markets. At Coupang, your base pay is one part of your total compensation.

The base pay for Staff position ranges from $152K/year to $261K/year. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.

General Description of All Benefits

• Annual bonus is 0-20% of the base salary
• Medical/Dental/Vision/Life, AD&D insurance
• Flexible Spending Accounts (FSA) & Health Savings Account (HSA)
• Long-term/Short-term Disability
• Employee Assistance Program (EAP) program
• 401K Plan with Company Match
• 18-21 days of the Paid Time Off (PTO) a year based on the tenure
• 12 Public Holidays
• 6 weeks Paid Parental leave
• Pre-tax commuter benefits
• MTV - [Free] Electric Car Charging Station

General Description of Other Compensation

“Other Compensation” includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale.

Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair styles), color, religion, religious creed (including religious dress and grooming practices), sex or gender (including pregnancy, childbirth, breastfeeding, and medical conditions related to pregnancy, childbirth or breastfeeding), gender identity, gender expression, sexual orientation, ,ancestry, national origin (including language use restrictions), age (40 and over), physical or mental disability, medical condition, genetic information, HIV/AIDS or Hepatitis C status, family status (including but not limited to marital or domestic partnership status), military or veteran status, use of a trained dog guide or service animal, political activities or affiliations, ancestry, citizenship, family and medical leave status, status as a victim of any violent crime, or any other characteristic or class protected by the laws or regulations in the locations where we operate.

Recruitment Process and Others

Recruitment Process

• Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer

• The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.

• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.

Details to Consider

• This job posting may be closed prior to the stated end date for application if all openings are filled.

• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.

• Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws.

Privacy Notice Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/

If you need assistance and/or a reasonable accommodation in the application of recruiting process due to a disability, please contact us at usrecruiting@coupang.com.

Requisition ID: R0072711

Please complete the attached Internal Transfer Request Form and submit.  

Please make sure to apply with your Coupang e-mail address.  

• Proactively drive the execution of end-to-end data platform, data quality roadmap for Search & Discovery ML efforts
• Take ownership, consolidate and optimize the Search & Discovery core data logging, processing, and ML model training pipelines
• Develop and scale data infrastructure that powers batch and real-time data processing, monitoring, and debugging for ML experiments
• Drive design and implementation of scalable ML based ranking system and online ML inference services
• Build strong cross-functional partnerships with Data Scientists, Analysts, Product Managers, Indexing Platform Engineers, Ranking Engineers and Data Platform Engineers to understand both the offline and online needs and deliver on those needs
• Research, analyze and select technical approaches for solving difficult and challenging development and integration problems, coach and mentor other engineers in process and methodologies

Basic Qualifications

• Bachelor's degree in computer science, electrical engineering, mathematics, statistics or
  closely related fields
• 8 years of professional experience in applied machine learning
• Experience in machine learning, deep learning, and statistical modeling
• Proficiency in Python and/or Java, with experience in building production grade ML
  systems

Preferred Qualifications

• Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning)
• Experience with Big Data tools and ETL frameworks (Hadoop, Hive, Presto, Spark, Scala, Apache Airflow, etc.)
• Experience with ML frameworks such as Tensorflow and Pytorch
• Experience with deploying highly robust and scalable data pipelines processing petabytes of data
• Experience in managing mission critical services with high throughput and low latency
• Experience with integrating applications and platforms with cloud technologies (i.e, AWS and GCP)
• Strong verbal and written communication skills
• Ability to conduct meetings and make professional presentations, and explain complex concepts and technical material to non-technical users
• Experience with machine learning platform for search & discovery related rankings is a strong plus

Pay & Benefits

Our compensation reflects the cost of living across several US geographic markets. At Coupang, your base pay is one part of your total compensation.

The base pay for this position ranges from $174,000K/year to $299,000K/year. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.

General Description of All Benefits

• Medical/Dental/Vision/Life, AD&D insurance
• Flexible Spending Accounts (FSA) & Health Savings Account (HSA)
• Long-term/Short-term Disability
• Employee Assistance Program (EAP) program
• 401K Plan with Company Match
• 18-21 days of the Paid Time Off (PTO) a year based on the tenure
• 12 Public Holidays
• Paid Parental leave
• Pre-tax commuter benefits
• MTV - [Free] Electric Car Charging Station

General Description of Other Compensation

"Other Compensation" includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale.

• Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer
• The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.
• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage

Details to Consider

• This job posting may be closed prior to the stated end date for application if all openings are filled.
• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.
• Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws.

Privacy Notice

• Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/

Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair styles), color, religion, religious creed (including religious dress and grooming practices), sex or gender (including pregnancy, childbirth, breastfeeding, and medical conditions related to pregnancy, childbirth or breastfeeding), gender identity, gender expression, sexual orientation, ,ancestry, national origin (including language use restrictions), age (40 and over), physical or mental disability, medical condition, genetic information, HIV/AIDS or Hepatitis C status, family status (including but not limited to marital or domestic partnership status), military or veteran status, use of a trained dog guide or service animal, political activities or affiliations, ancestry, citizenship, family and medical leave status, status as a victim of any violent crime, or any other characteristic or class protected by the laws or regulations in the locations where we operate. Coupang is also committed to providing a safe work environment for its employees and its consumers.

If you need assistance and/or a reasonable accommodation in the application of recruiting process due to a disability, please contact us at usrecruiting@coupang.com.

Job Requisition ID: R0069890

Please complete the attached Internal Transfer Request Form and submit.  

Please make sure to apply with your Coupang e-mail address.  

• Proactively drive the execution of end-to-end data platform, data quality roadmap for Search & Discovery ML efforts
• Take ownership, consolidate and optimize the Search & Discovery core data logging, processing, and ML model training pipelines
• Develop and scale data infrastructure that powers batch and real-time data processing, monitoring, and debugging for ML experiments
• Drive design and implementation of scalable ML based ranking system and online ML inference services
• Build strong cross-functional partnerships with Data Scientists, Analysts, Product Managers, Indexing Platform Engineers, Ranking Engineers and Data Platform Engineers to understand both the offline and online needs and deliver on those needs
• Research, analyze and select technical approaches for solving difficult and challenging development and integration problems, coach and mentor other engineers in process and methodologies

Basic Qualifications

• Bachelor's degree in computer science, electrical engineering, mathematics, statistics or 
  closely related fields
• 8 years of professional experience in applied machine learning
• Experience in machine learning, deep learning, and statistical modeling
• Proficiency in Python and/or Java, with experience in building production grade ML 
  systems

Preferred Qualifications

• Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning)
• Experience with Big Data tools and ETL frameworks (Hadoop, Hive, Presto, Spark, Scala, Apache Airflow, etc.)
• Experience with ML frameworks such as Tensorflow and Pytorch
• Experience with deploying highly robust and scalable data pipelines processing petabytes of data
• Experience in managing mission critical services with high throughput and low latency
• Experience with integrating applications and platforms with cloud technologies (i.e, AWS and GCP)
• Strong verbal and written communication skills
• Ability to conduct meetings and make professional presentations, and explain complex concepts and technical material to non-technical users
• Experience with machine learning platform for search & discovery related rankings is a strong plus

Pay & Benefits

Our compensation reflects the cost of living across several US geographic markets. At Coupang, your base pay is one part of your total compensation.

The base pay for this position ranges from $174,000K/year to $299,000K/year. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.

General Description of All Benefits

• Medical/Dental/Vision/Life, AD&D insurance
• Flexible Spending Accounts (FSA) & Health Savings Account (HSA)
• Long-term/Short-term Disability
• Employee Assistance Program (EAP) program
• 401K Plan with Company Match
• 18-21 days of the Paid Time Off (PTO) a year based on the tenure
• 12 Public Holidays
• Paid Parental leave
• Pre-tax commuter benefits
• MTV - [Free] Electric Car Charging Station

General Description of Other Compensation

"Other Compensation" includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale. 

• Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer
• The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.
• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage

Details to Consider

• This job posting may be closed prior to the stated end date for application if all openings are filled.
• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.
• Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws.

Privacy Notice

• Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/

Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair styles), color, religion, religious creed (including religious dress and grooming practices), sex or gender (including pregnancy, childbirth, breastfeeding, and medical conditions related to pregnancy, childbirth or breastfeeding), gender identity, gender expression, sexual orientation, ,ancestry, national origin (including language use restrictions), age (40 and over), physical or mental disability, medical condition, genetic information, HIV/AIDS or Hepatitis C status, family status (including but not limited to marital or domestic partnership status), military or veteran status, use of a trained dog guide or service animal, political activities or affiliations, ancestry, citizenship, family and medical leave status, status as a victim of any violent crime, or any other characteristic or class protected by the laws or regulations in the locations where we operate. Coupang is also committed to providing a safe work environment for its employees and its consumers.

If you need assistance and/or a reasonable accommodation in the application of recruiting process due to a disability, please contact us at usrecruiting@coupang.com.

Job Requisition ID: R0069890

About the team 

Roku continues a long-term trajectory of supporting summer internship roles within the organization, to provide applicants with experience of our enthusiasm, expertise and company culture.  

The Advanced Development, AI @ Roku (Roku’s internal AI productivity suite), Content, and Edge AI team within the Engineering organization has been granted 4 summer internships in 2026. These internships are only available to postgraduate students who are currently studying for a PhD or have recently successfully completed a PhD. 

About the role 

The purpose of the internship is to involve each student in the work of the AI @ Roku, Content, and Edge AI team. Each student will be assigned a supervisor among the senior members of the team. This supervision aims to ensure that the intern finishes their contract with a clear view of what permanent employment within the AI @ Roku, Content, and Edge AI team would be like.  

All the intern projects will be clearly defined at the outset, easy to get started and challenging to complete, and relevant to the AI @ Roku, Content, and Edge AI team’s Q3 and Q4 2026 goals. 

Project outlines: 

• Multi-modal AI: Building new experiences for TVs with Vision-Language Models, focusing on multi-modal understanding and natural language interaction. 
• Developing multimedia content generation techniques 
• Content understanding and analysis for episodic and cinematic videos 
• Deploying and optimizing AI algorithms to the edge 
• Using AI to improve image and video quality 

What you'll be doing 

• Define, agree, and track the meaningful measures (KPIs) of success in the project.  
• Collaborate effectively with other members of the project team to achieve goals.  
• Contribute meaningfully to the implementation and delivery of the project.  
• Identify and communicate key learnings within and across project teams.  
• Actively partner with experts in the project field in Cambridge, and internationally.  
• Conduct feasibility studies to determine if 'new-to-the-world' ideas are viable for long-term integration into the Roku OS. 
• Present findings and prototypes to the broader engineering and product leadership to influence Roku’s long-term innovation roadmap.

We’re excited if you have 

• PhD in STEM discipline (current or recent)  
• Strong problem-solving and analytical skills 
• Excellent communication and presentation skills 
• Proficient in data visualization and interpretation 
• High-level proficiency in Python 
• Background in mathematics, statistics, and CS 
• Ability to design and implement machine learning models 
• Familiarity with machine learning frameworks (PyTorch, Tensorflow) 
• Understanding of machine learning and computer vision algorithms and techniques 
• Experience with image processing pipelines, embedding spaces or modern LLM orchestration frameworks (e.g., LangChain, LlamaIndex, LangFuse) is a plus. 

About the team

Roku pioneered in developing operating system for TVs. Tv Technology team within Roku TV NPI group is focused on building TVs with best Audio Quality , Picture Quality and Enable innovation technology. 
With tens of million TVs sold across many countries, thousands of streaming channels and billions of hours watched over the Roku TVs, having best Audio Quality & Picture Quality is crucial.

About the role

We are looking for a creative and passionate Machine Learning (ML) / Deep Learning (DL) Engineer to join the Roku TV team. You will explore Picture Quality (PQ) and Audio Quality (AQ) improvements using AI in a resource-constrained environment, pushing the limits of what is possible in TV technology. Additionally, this role will involve applying state-of-the-art ML techniques to different PQ and AQ related tasks. You will play a critical role in building scalable systems and algorithms that enhance streaming experiences for millions of users worldwide.

For California Only - The estimated annual salary for this position is between $135,000 - $185,000 annually. Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off.

What you’ll be doing

• Explore and implement AI-driven Picture Quality and Audio Quality improvements for Roku TV
• Apply advanced ML techniques in the optimization of models to enhance video and audio processing pipelines
• Research, understand, and implement technical papers on ML/DL to solve complex challenges
• Collaborate with engineering teams and cross-functional partners to translate business requirements into technical solutions
• Run experiments and analyze results against critical business KPIs

We’re Excited If You Have

• 1+ years of experience applying Machine Learning to concrete problems at scale
• Strong CS fundamentals; ability to write efficient algorithms with ease
• Hands-on experience with Machine Learning / Deep Learning frameworks like TensorFlow or PyTorch
• Proficiency in coding firmware development using C++
• Proficiency in coding ML/DL algorithms using Python
• Solid understanding of DL fundamentals, including RNN, CNN, and multi-modal models
• Familiarity with video processing pipelines and audio pipelines
• AI literacy and curiosity—whether through previous work or personal exploration
• Excellent communication skills, strong work ethic, and works with minimum supervision 
• Have hands-on experience with edge devices like jetson nano, kadas, and similar
• Research background preferred; ability to understand and implement technical papers on ML/DL
• Published papers in the field of ML/DL are a bonus
• Hold an MS or PhD in Computer Science, Electrical Engineering, or related fields - Preferred

#LI-DH2

About the team

• ML infrastructure: Help build a first-class machine learning platform from the ground up which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality.
• Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models.
• Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy.
• Production operations: Low-level systems debugging, performance measurement, and optimisation on large production clusters.
  Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions.
  Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends.

• Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field.
• Experience in applied machine learning on real use cases (bonus points for ad tech-related use cases). Min 5 years of total experience.
• Great coding skills and strong software development experience (we use Spark, Python, Java).
• Familiarity with real-time evaluation of models with low latency constraints.
• Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc.
• Ability to work with large scale computing frameworks, data analysis systems, and modelling environments. Examples include Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB.
• Ad tech background is a plus.
• AI growth mindset / AI Literacy
  
  
  

About the Team

The Advanced Development team at Roku pushes beyond today’s product lines to invent the next generation of intelligent and generative media systems.
We explore ideas that sit years ahead of production, developing foundational technologies that will redefine how content is understood, created, and personalised across millions of Roku devices.

This is a rare environment — a PhD-level, cross-disciplinary group combining machine learning research, software engineering, and DevOps.
Everyone here is an expert, but not narrowly focused. The team blends deep technical mastery with broad creative vision — people who challenge convention, embrace ambiguity, and build what’s never been built before. It’s a collaborative, low-ego, ownership-driven culture built on trust and curiosity.

We’re seeking an Applied Scientist with a strong foundation in mathematics, machine learning, and computer science, combined with experience in cloud engineering, DevOps, and computer vision — someone who thrives where research meets production.

About the Role

As a Senior Applied Machine Learning Engineer, you’ll help design, build, and deploy the systems that make media smarter.
You’ll work across the full model and software lifecycle, from prototype to production, developing scalable ML pipelines and cloud architectures that power generative AI, intelligent media understanding, content analysis, and advertising intelligence.

You’ll operate at the intersection of machine learning, infrastructure, and software engineering, taking ownership from data collection through deployment — and seeing your work directly influence how audiences experience Roku’s content and advertising ecosystem.

What You’ll Be Doing

• Deploying scalable, fault-tolerant computer vision, media understanding, and generative AI systems to production

• Overseeing the full model development cycle: ideation, prototyping, implementation, deployment, testing, and operations

• Designing uncertainty metrics and communicating results to both technical and non-technical stakeholders

• Gathering and compiling datasets, defining annotation ontologies, auditing annotation operations, and ensuring data quality

• Staying up to date with industry and academic trends in computer vision, machine learning, and generative models for media and advertising

• Working closely with product and other engineering teams to implement new content and advertising experiences through cloud services

• Integrating services from other teams around the company, while also providing reusable ML services to others

• Evaluating and providing feedback on new platform technologies provided by internal teams

• Working with QA teams to address bugs and contribute to automation and quality assurance

We’re Excited If You Have

• A Master’s degree (PhD preferred) in Computer Science, Applied Mathematics, or a related field

• Strong background developing applied machine learning systems using PyTorch or TensorFlow

• Expertise in image processing, computer vision, or natural language processing

• Experience using AWS, GCP, or Azure for storing data, training, and serving models

• Proven ability to evaluate models and communicate insights effectively

• Experience building APIs with frameworks such as GraphQL or REST

• Experience with workflow orchestration tools such as Airflow, Argo, AWS Step Functions, or Metaflow

• Hands-on experience with Docker, Kubernetes, Terraform, CloudFormation, CI/CD automation, and Python build or packaging tools

#LI-PA1

About the team

Roku pioneered streaming to the TV and continues to innovate and lead the industry. While we are well-positioned to help shape the future of television – including TV advertising – around the world, continued success relies on its investment in our machine learning capabilities. Roku offers millions of options to our users: movies, episodes, news, sports, and channels from all around the world. The Roku Content Platform is key to onboarding content into the Roku ecosystem, delighting our customers. Building a content knowledge platform that provides insights to downstream systems like Search, Recommendations, Ads, and Voice to shape customers' experiences is why we’re actively looking for a Senior Machine Learning / AI Engineer, Content Platform who can drive further innovation.

About the role

Roku pioneered streaming to the TV and continues to innovate and lead the industry. While we are well-positioned to help shape the future of television – including TV advertising – around the world, continued success relies on its investment in our machine learning capabilities.

Roku offers millions of options to our users: movies, shows, news, sports, and channels from all around the world. The Roku Content Platform is key to onboarding content into the Roku ecosystem, delighting our customers. Building a content knowledge platform that provides insights to downstream systems like Search, Recommendations, Ads, and Voice to shape customers' experiences is why we’re actively looking for a Senior Machine Learning / AI Engineering Manager who can drive further innovation.

The person in this role will leverage their technical skills, business intuition, and analytical thinking to build best-in-class machine learning products. This includes integrating Generative AI capabilities and Vision Models to enrich content quality and user engagement. This is a role for a hardened ML practitioner and a highly skilled organizational leader. You will be the architectural and operational bridge between deep AI research and fast-paced product delivery. If you know how to build a 3-year vision for content discovery and translate it into 2-week shipping cycles, this is your team.

What you’ll be doing

• Vision & Strategy: Define the roadmap for ML within the Content Platform..
• Technical Leadership: Provide high-level guidance on model architecture and MLOps infrastructure to ensure models are scalable and production-ready.
• People Management: Lead and grow a high-performing team of ML Engineers. You will be responsible for career development, performance management, and maintaining a culture of rigorous experimentation.
• Cross-Functional Execution: Partner with Product, Engineering, and Content Strategy to align ML initiatives with business goals.
• Delivery: Champion a culture of incremental delivery. Balance long-term R&D with the need to deliver measurable improvements to the production environment.

We’re excited if you have

• 10+ years of total experience in software/ML engineering, with at least 5 years in a dedicated people management leadership role.
• MS or BS in Computer Science, Mathematics, or a related quantitative field.
• You possess a deep theoretical and practical understanding of Machine Learning. 
• You understand the business of streaming and how ML directly impacts the bottom line.
• You can explain complex model to a non-technical executive just as easily as you can debate architecture with a seasoned engineer.
• Proven track record of deploying large-scale ML models into production environments.
• Experience with modern ML stacks (e.g., LLMs, PyTorch, TensorFlow, Spark, and cloud-native MLOps tools).

Staff AI Engineer

About Us:

BlackSky is a real-time intelligence company. We own and operate the world's most advanced space-based intelligence platform and provide customers satellite imagery, automated analytics and high-frequency monitoring of strategic locations, economic assets and events from around the globe. BlackSky is trusted by the most demanding allied military and intelligence organizations and commercial companies to deliver foresight into critical matters that affect national security and the economy. BlackSky's data enables governments and businesses to see, understand and anticipate change as it happens, giving them the ultimate strategic advantage so they can act quickly. Our global team works with cutting-edge technology to make a difference around the world and prides itself on being people-first, customer-focused and fun.

BlackSky is seeking a Staff AI Engineer to lead the architecture, development, and delivery of mission-critical AI solutions within customer environments. This is a hands-on role and an opportunity to develop and shape an exciting new growth area. The ideal candidate for this role blends deep technical ownership (roadmap, R&D, AI/ML systems) with customer-facing solution delivery (scoping, prototype-to-production, and executive communication). This senior individual contributor role will partner with CV, MLOps, Data QA, Solutions, and BD teams to ensure BlackSky delivers reliable and actionable insights. The role will be full-time based out of Herndon, VA working in our SCIF with occasional customer site commitments and will report to the Senior Manager of AI.

Responsibilities:

• Partner with CV and MLOps to design and extend components needed to ensure models are trained, versioned, deployed, monitored, and maintained reliably in customer environments.
• Collaborate with the Data QA team to define annotation standards, resolve taxonomy issues, and identify data-quality improvements based on model failure modes.
• Independently prototype, evaluate, and deploy AI capabilities in a secure development environment.
• Communicate technical strategy, progress, risks, and opportunities clearly to leadership, cross-functional partners, and other stakeholders.
• Contribute to proposals, white papers, and long-term strategy, shaping future mission-aligned geospatial AI investments.
• Own and architect the mission-aligned roadmap for geospatial CV and applied AI, partnering with customers to translate mission requirements into technical designs and implementing core components.
• Other job-related duties as assigned.

Required Qualifications:

• Minimum of 10 years of hands-on software engineering experience, including at least 4+ years developing and deploying applied AI/ML systems and pipelines.
• Bachelor’s degree in CS/EE/math/statistics or a related quantitative field.
• Strong proficiency in Python and modern ML/CV libraries such as PyTorch or TensorFlow.
• Experience researching, building, and evaluating production-ready CV models for detection, segmentation, change detection, or related tasks.
• Experience working with remote sensing imagery including geometry, radiometric normalization, augmentation, and sensor-specific challenges.
• Hands-on experience with geospatial tools such as GDAL, Rasterio, GeoPandas, Shapely, xarray, or Zarr.
• Experience with modern ML infrastructure, including cloud services (e.g., AWS), containerization and orchestration platforms (e.g., Kubernetes), and the ability to adapt these systems to customer-specific or offline environments such as secure enclaves, on-prem systems, or air-gapped deployments.
• Strong ability to communicate complex technical concepts to diverse audiences including leadership and technical teams.
• Must have an active US Top Secret clearance with an SCI.

Preferred Qualifications:

• M.S. or Ph.D. in CS/EE/math/statistics or a related quantitative field.
• At least 2 years of experience designing, building, and operating AI/ML systems in secure, on-prem and/or air-gapped systems.
• Experience working in DoD, IC, or Fed/Civ environments.
• Experience designing and executing large-scale CV experiments, including dataset construction, synthetic/augmented data generation, and evaluation protocols tailored to mission needs.
• Familiarity with remote sensing data sources including BlackSky, Airbus, Planet, and Vantor.
• Demonstrated experience designing CV/ML systems and pipelines that meet or exceed benchmarks and are optimized for production constraints such as latency and efficiency.
• Exposure to foundation models, VLMs, and other multimodal approaches for geospatial imagery.

Life at BlackSky for full-time US benefits eligible employees includes:

• Medical, dental, vision, disability, group term life and AD&D, voluntary life and AD&D insurance
• BlackSky pays 100% of employee-only premiums for medical, dental and vision and contributes $100/month for out-of-pocket expenses!
• 15 days of PTO, 11 Company holidays, four Floating Holidays (pro-rated based on hire date), one day of paid volunteerism leave per year, parental leave and more
• 401(k) pre-tax and Roth deferral options with employer match
• Flexible Spending Accounts
• Employee Stock Purchase Program
• Employee Assistance and Travel Assistance Programs
• Employer matching donations
• Professional development
• Mac or PC? Your choice!
• Awesome swag

BlackSky is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action Employer All Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, sexual orientation, gender identity, disability, protected veteran status or any other characteristic protected by law.

To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. 

EEO/AAP/ Pay Transparency Statements:

https://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf

https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf

At Counterpart Health, we are transforming healthcare and improving patient care with our innovative primary care tool, Counterpart Assistant. By supporting Primary Care Physicians (PCPs), we are able to deliver improved outcomes to our patients at a lower cost through early diagnosis and longitudinal care management of chronic conditions.

We’re looking for a Senior Machine Learning Engineer to help us build a revolutionary new health care business. Clover uses Machine Learning/Natural Language Processing to leverage our data to help keep beneficiaries healthy and out of the hospital by getting them targeted care. By predicting avoidable adverse events, our ML/NLP/LLM infrastructure is central to working on our central mission, and has a direct impact on our beneficiaries. You will help build systems and tools that support the data needs of a diverse organization and contribute to the expansion of the Machine Learning/Natural Language Processing/LLM capabilities of our Data Platform.

As a Senior Machine Learning Engineer, you will:

• Design, implement and validate high-reliability, distributed platforms for machine learning,  natural language processing, and LLMs.
• Create, debug, interpret and improve production machine learning and natural language processing models.
• Build the tools and validation processes that help Clover translate insights into action at scale.
• Use existing commercial and open source tools to create a robust production platform.
• Work closely with Clover's Data Science and Engineering teams to ensure that the ML/NLP/LLM Platform is providing real value.
• Document, iterate, and provide tutorials to ensure Data Scientists are able to use your tools easily.

You will love this job if:

• You want to create impact with your work by finding machine learning-driven insights in the data to unlock value and improve health outcomes for real people.
• You are comfortable acting autonomously in ambiguous and changing environments.
• You value collaboration and feedback. You can communicate technical vision in clear terms— to your teammates and across the technology team more broadly. You are willing and able to help your teammates grow by demonstrating best practices, providing (and receiving) respectful and constructive feedback, and disclosing your unique insights with everyone.
• You enjoy working in a fluid environment, defining priorities that adapt to our larger goals. You can bring clarity to ambiguity while remaining open-minded to new information that might change your mind.
• You are not hesitant to jump in to help fix things that are broken and you are encouraged to make sustainable systems. You are happy to fill in the gaps to reach a goal where necessary, even if it does not always fit your job description.
• You have a genuine interest in what good technology can do to help people and have a positive attitude about tackling hard problems in an important industry.

You should get in touch if:

• You have 5+ years of experience in Machine Learning Engineering roles in technology enabled companies. Healthcare experience preferred but not required.
• You have experience with Python, Python data science libraries (numpy, pandas, sklearn, tensorflow, pytorch, etc.), and deploying Python apps into production environments.
• You have experience with Natural Language Processing and/or LLMs.
• You have a solid foundation in feature engineering, feature selection, and machine learning techniques.
• You have experience interpreting, modifying, and debugging the inputs and outputs of production ML/NLP/LLM models.
• You have both built and refactored complex distributed systems, especially ML/NLP/LLM systems.
• You have scaled the impact of other engineers and data scientists through mentorship, development of reusable libraries, and documentation.

Benefits Overview: 

• Financial Well-Being: Our commitment to attracting and retaining top talent begins with a competitive base salary and equity opportunities. Additionally, we offer a performance-based bonus program, 401k matching, and regular compensation reviews to recognize and reward exceptional contributions.
• Physical Well-Being: We prioritize the health and well-being of our employees and their families by providing comprehensive medical, dental, and vision coverage. Your health matters to us, and we invest in ensuring you have access to quality healthcare.
• Mental Well-Being: We understand the importance of mental health in fostering productivity and maintaining work-life balance. To support this, we offer initiatives such as No-Meeting Fridays, monthly company holidays, access to mental health resources, and a generous flexible time-off policy. Additionally, we embrace a remote-first culture that supports collaboration and flexibility, allowing our team members to thrive from any location. 
• Professional Development: Developing internal talent is a priority for Clover. We offer learning programs, mentorship, professional development funding, and regular performance feedback and reviews.

Additional Perks:

• Employee Stock Purchase Plan (ESPP) offering discounted equity opportunities
• Reimbursement for office setup expenses
• Monthly cell phone & internet stipend
• Remote-first culture, enabling collaboration with global teams
• Paid parental leave for all new parents
• And much more!

About Counterpart Health: In 2018, Clover Health set out to do something unprecedented: build a clinically intuitive, AI-enabled solution that fits within physicians' workflows to help support the earlier diagnosis and management of chronic conditions.

Years later, that vision is a reality, with thousands of practitioners using Counterpart Assistant during patient visits to improve disease management, reduce medical expenses, and drive success in value-based care.

With an exceptional team of value-based care and technology experts, Counterpart Health is driving value-based care at the speed of software.

Counterpart Health is a subsidiary of Clover Health. From Clover’s inception, Diversity & Inclusion have always been key to our success. We are an Equal Opportunity Employer and our employees are people with different strengths, experiences, perspectives, opinions, and backgrounds, who share a passion for improving people's lives. Diversity not only includes race and gender identity, but also age, disability status, veteran status, sexual orientation, religion and many other parts of one’s identity. All of our employee’s points of view are key to our success, and inclusion is everyone's responsibility.

#LI-REMOTE

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. We are an E-Verify company.

Local or 100% Remote

About Point

✨ Real Impact, Real People: Our mission at Point is to make homeownership more valuable and accessible. Your work directly helps homeowners access their wealth, achieve financial flexibility, and realize life changing goals.

✨ Funding: With over $175M raised from top investors like Andreessen Horowitz, WestCap, Greylock, and Prudential, we’re scaling fast! You have the opportunity to join us at a pivotal stage.

✨ Game-changing Product: We're building a category defining company in home equity. We’ve earned a 4.7 Trustpilot rating and an A+ from the BBB, a testament to the value we provide to our 20,000+ customers.

✨ Great Place to Work: Our employees love working here! We are a Certified Great Place to Work and a Fortune Best Workplaces in the Bay Area.

✨ Remote First Culture, Genuine Connection: Work from anywhere in the U.S., while staying closely connected through virtual collaboration, team gatherings, and a people-first culture.

About the role

We are seeking a Senior Data Scientist to join our growing data team. You will be responsible for building and deploying machine learning models to support marketing, customer engagement, and core business use cases. You will also monitor model performance, identify and mitigate model deterioration, forecast business outcomes, and maintain high-quality datasets. The role is not limited to working on machine learning and model development and will also include, but not limited to advanced analytics projects such as root-cause analysis, identifying trends in data and building time-series projections.

This is an excellent opportunity for someone advanced in their data science career to enhance their skills in applied machine learning while working closely with other senior data scientists, engineers, and cross-functional stakeholders.

Your responsibilities

• Model Development: Design, train, and deploy machine learning models to solve marketing, forecasting, and operational problems.
• Model Monitoring: Track model performance, detect drift or deterioration, and assist in implementing corrective measures.
• Forecasting & Insights: Develop time-series forecasts and performance dashboards that inform marketing strategies and broader business decisions.
• Data Management: Clean, transform, and maintain structured datasets to ensure reliable inputs for analytics and modeling.
• Collaboration: Partner with marketing, product, and business teams to translate requirements into data-driven solutions.
• Documentation & Reporting: Produce clear documentation of models, data pipelines, and processes; communicate insights to technical and non-technical stakeholders.

About you

• 4–5 years of hands-on experience in data science, applied statistics, or machine learning (internships and projects count).
• Familiarity and experience working with AI toolkits such as Claude or Gemini or similar.
• Strong foundation in Python (Pandas, NumPy, scikit-learn; PyTorch or TensorFlow a plus).
• Familiarity with SQL for data extraction and transformation.
• Experience with basic machine learning workflows: preprocessing, feature engineering, model training, validation, and monitoring.
• Understanding of data visualization tools (e.g., Tableau, Power BI, or Python libraries like Matplotlib/Seaborn).
• Curiosity and eagerness to learn — especially around model monitoring, forecasting, and marketing analytics.
• Excellent problem-solving skills and ability to communicate insights clearly.
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field. A Master’s degree is a plus.
• Advanced knowledge of AI tools
• Expertise on BI dashboarding tools such as Sigma or Tableau preferred
• Strong Excel skills

Our benefits 

• Generous health benefits: We provide comprehensive medical, dental, and vision plans with options for flexible spending accounts (FSA) and health savings accounts (HSA).
• Unlimited paid time off: Recharge with unlimited paid time off and 10 company holidays. 
• Flexible remote and onsite work: Our teams work from many different locations and time zones. We support fully remote work and also have an amazing in-person environment in our downtown Palo Alto, CA HQ. 
• Fully paid parental leave: Point will supplement state Paid Family Leave (PFL) so employees receive 100% of their regular base pay, plus two additional weeks of fully paid leave after state PFL ends. In states without PFL, Point offers up to 8 weeks of paid parental leave. In addition, employees also receive 4 weeks of fully paid transition time, during which you may work 2–3 days per week while receiving full base pay.
• Equity: We offer meaningful equity because we believe in sharing the value you help create. Your contributions directly impact our growth, and your equity gives you a stake in our future success. 
• Financial wellness:  We provide 401K retirement plans for employees as well as guaranteed life insurance and short- and long-term disability coverage. 
• Extra work/life benefits: We provide monthly stipends for internet, mobile plans, wellness perks, a one-time home office reimbursement, and company provided equipment including a MacBook and monitor.

Compensation at Point will be determined by skills, experience, and geographic location. Point has identified the expected annual base salary for this role at this level based on the market by tiers (Region | Location | Market Salary):

• Tier 1 | San Francisco Bay Area, New York, and Seattle | $179,550 - $198,450
• Tier 2 | Chicago, Austin, Denver, Boston, Washington DC, San Diego, Portland, Sacramento, Philadelphia, Los Angeles & Santa Barbara | $164,350 - $181,65
• Tier 3 | All other US metro areas | $156,750 - $173,250

This does not include any other potential components of the compensation package, including equity, benefits, and perks outlined above. At the launch of each position, we benchmark compensation to the appropriate role and level utilizing competitive compensation data from various data sources as references. At the offer stage, we use the signal we received from our interviews, coupled with your experience, location, and other job-related factors, to determine final compensation.

Location Requirement: This is a remote position. However, candidates must reside in one of Point’s states of operation: AL, AZ, AR, CA, CO, CT, DC, FL, GA, IL, KS, KY, MA, MD, MI, MN, MO, NH, NV, NJ, NY, NC, OH, OR, PA, SC, TN, TX, UT, VA, WA, WI.

Point is proud to be an equal-opportunity employer. We provide employment opportunities regardless of age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. Each individual at Point brings their own perspectives, work experiences, lifestyles, and cultures with them, and we believe that a more diverse team creates more innovative products, provides better services to customers, and helps us all grow and learn. 

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Supportability Evaluation team acts as stewards of the financial ecosystem. Our mission is to protect Stripe’s reputation with our global financial partners by architecting highly precise, automated supportability controls. We develop the AI/ML models and systems that detect and action supportability violations in real-time. We're responsible for building high-fidelity detection engines that ensure our merchants remain compliant across the globe, balancing the scale of millions of users with the surgical precision required by the world’s largest financial institutions.

What you’ll do

As a Machine Learning Engineer in Supportability, you will be responsible for designing, building, training, evaluating, deploying, and owning AI/ML models in production. You will work closely with software engineers, machine learning engineers, product managers, and data scientists to operate Stripe’s ML powered systems, features, and products. You will also have the opportunity to contribute to and influence AI/ML architecture at Stripe and be a part of a larger community.

Responsibilities

• Design state-of-the-art AI/ML models and large scale systems for detection and decisioning for Stripe products based onAI/ML principles, domain knowledge, and engineering constraints
• Drive the expansion of Stripe's largest LLM-based system, scaling its usage and integrating new capabilities through agentic approaches or supervised learning.
• Rapidly prototype new AI/ML-based approaches to achieve key business goals.  
• Develop processes to train and evaluate models in offline and online environments
• Integrate models into production systems and ensure their scalability and reliability
• Collaborate with product and strategy partners to propose, prioritize, and implement new product features
• Engage with the latest developments in AI/ML and take calculated risks in transforming innovative ideas into productionized solutions
• Explore cutting-edge AI/ML techniques and evaluate their potential to solve business problems

Who you are

We are looking for ML Engineers who are passionate about building AI/ML and AI systems that touch the lives of millions. You have experience building and evaluating advanced AI/ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.

Minimum requirements

• 2+ years of industry experience building and shipping AI/ML systems in production
• Proficient with AI/ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark 
• Knowledge of various AI/ML algorithms and model architectures
• Hands-on experience in designing, training, and evaluating machine learning models
• Hands-on experience in productionizing and deploying models at scale
• Experience rigorously evaluating model performance, including cleaning data, and working with data-generating processes to improve signal and reduce noise in high-noise datasets.
• Proficiency in creatively applying modern machine learning techniques and Generative AI models to solve complex business problems.

Preferred qualifications

• MS/PhD degree in AI/ML or related field (e.g. math, physics, statistics)
• Experience with DNNs including the latest architectures such as transformers and LLMs
• Experience working in Java or Ruby codebases
• Proven track record of building and deploying AI/ML systems that have effectively solved ambiguous business problems
• Experience with online experimentation such as A/B testing or multi-armed bandits.
• Experience with model calibration

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a large amount of work ahead of us. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

At its core, Stripe is a treasury company, and the Treasury Finance team is key to building the Global Payments and Treasury Network (GPTN). We collaborate closely with product, engineering, sales, and finance teams to create innovative solutions that enhance Stripe’s financial capabilities. As part of Treasury Finance’s AI and Quantitative Analytics team, you’ll work at the intersection of finance, artificial intelligence, and product development, building autonomous agents and intelligent solutions that enhance treasury capabilities and enable us to better serve and scale with the global economy. 

What you’ll do

Within Treasury Finance AI and Quantitative Analytics, you’ll leverage your finance, artificial intelligence (AI) and technical expertise to  build intelligent solutions that enhance Stripe’s treasury capabilities. You’ll develop AI-powered tools and models, autonomous agents, and data products that automate complex workflows and provide actionable insights. Working at the intersection of artificial intelligence, finance, and product development, you'll collaborate with engineering and finance teams to deploy cutting-edge AI applications that scale with Stripe's growth and support our mission of increasing the GDP of the internet. 

You will:

• Apply your treasury and finance domain expertise to identify high-impact opportunities where AI can solve complex liquidity, financial risk management, and capital markets challenges
• Build AI-powered tools and artificial intelligence solutions to automate treasury workflows, enhance risk management, and scale our operations
• Design and deploy autonomous AI agents using frameworks like LangChain and LangGraph that can reason, plan, and execute complex multi-step problems and enhance decision-making processes
• Ship interactive data products and self-service analytics tools using Python frameworks (Streamlit, Dash, Gradio) that provide real-time insights and actionable intelligence
• Work across Treasury Finance and partner teams to scope AI-driven solutions that allow us to optimize and scale our most complex, data-driven financial workflows 
• Explore and prototype cutting-edge AI applications to address emerging treasury challenges

Who you are

We’re looking for a technical problem-solver with treasury and finance expertise who’s excited about leveraging artificial intelligence and who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• Bachelor’s degree in finance, mathematics, computer science, engineering or a related field with 5+ years of work experience in software development, data science, or treasury/finance roles 
• Working familiarity with financial risk and capital markets concepts
• Proficiency in Python and experience with AI/ML frameworks including PyTorch and TensorFlow
• Experience with Large Language Model (LLM) frameworks such as LangChain, LangGraph, or similar tools for building AI applications and agentic systems
• Solid understanding of AI fundamentals, including deep learning, natural language processing, and generative AI
• Results-oriented, with a focus on delivering impact in a fast-paced environment 
• Excellent communication skills for collaborating with product, engineering, and finance teams
• Highly organized, with attention to detail and the ability to manage tight deadlines

Nice to have

• • Familiarity with the payments industry and fintech landscape
  • Experience with Databricks and cloud platforms (AWS, GCP, Azure) 
  • Software engineering experience in Ruby

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Support Experience engineering organization builds and improves Stripe’s user support from end to end: how users get help within our products, how they get in touch with us when they have questions, and how our teams use internal tools to answer those questions. We’re accountable for the quality and reliability of this support stack and we use data and firsthand user research to continuously improve it. 

Providing great support to users of all sizes is culturally important to everyone at Stripe. We are a group of friendly, user-oriented engineers that partner closely with Stripe’s world-class design, product, and operational teams. This includes the external-facing support interfaces (support.stripe.com), content, entry points, internal tooling, case routing, and helping product teams across the company reduce support volume by improving our products. We are also using the latest generative AI technologies to re-imagine support experiences, and are developing AI assistants for Stripe’s users and internally to help our operations teams be more productive.

What you’ll do

As a Machine Learning Engineer on the Support Experience team, you'll play a crucial role in enhancing our self-serve support experiences. You will be responsible for designing, building, training, evaluating, deploying, and owning ML models in production. For example, we apply LLMs to answer user questions with conversational agents and personalize product documentation, and are building automated systems to solve complex user problems. You will work closely with software engineers, machine learning engineers, product managers, and data scientists to operate Stripe’s ML powered systems, features, and products. You will also have the opportunity to contribute to and influence ML architecture at Stripe and be a part of a larger ML community. 

Responsibilities

• Design  and implement state-of-the-art ML models and large scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints
• Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost
• Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction
• Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency
• Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities
• Stay current with the latest developments in ML/AI, particularly in natural language processing and conversational AI, and apply innovative ideas to improve support experiences

Who you are

We are looking for ML Engineers who are passionate about building ML systems that touch the lives of millions. You have experience developing efficient feature pipelines, building advanced ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.

Minimum requirements

• Bachelor's Degree in ML/AI or related field (e.g. math, physics, statistics)
• 3+ years in AI/ML and backend engineering, including building and operating production ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design.
• Deep and up-to-date applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration architectures, post-training methods, code generation, benchmarks and evaluations, etc. Familiarity with classical ML methods and common frameworks e.g. Pytorch, TensorFlow.
• Proficient in Python; strong distributed systems and data science fundamentals.
• Experience working closely with product management, design, other engineers, and other cross-functional partners.
• Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.

Preferred qualifications

• MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)
• Experience working in Java or Ruby codebases
• Experience designing, deploying, and owning Agentic LLM solutions (e.g., multi-step orchestrators, tool use/function calling) specifically for complex customer support or internal workflow automation.
• Comfortable working with distributed teams across multiple locations and time zones

SPACE SYSTEMS

At Rocket Lab, we’re not just launching rockets — we're building the future of space. Our Space Systems team builds everything from complete spacecraft, precision payloads to the components and subsystems that allow them to thrive in space, like solar panels, flight software, star trackers, optical systems, separation systems, radios, and more.

Our Space Systems team has enabled more than 1,700 missions, ranging from interplanetary exploration, in-space manufacturing to national security and defense initiatives. The team has built spacecraft, payloads, and components for missions to the Moon and Mars, working with partners including NASA, the Space Development Agency, and the U.S. Space Force. Whether it’s a single high-performance spacecraft, constellation, or the vertically integrated components that help them get to space — our world class Space Systems team is empowering some of the boldest and most ambitious space missions. 

SENIOR SOFTWARE ENGINEER I/II – AI ENGINEERING

Are you passionate about advancing aerospace technology through cutting-edge software solutions? We're looking for a driven Senior Software Engineer I/II to join our AI Engineering team, based out of our Littleton, CO site. You will play a pivotal role in developing AI-driven tools and applications, including those leveraging Large Language Models (LLMs) and advanced machine learning techniques. This role offers the opportunity to work on innovative projects that integrate AI applications to enhance aerospace systems, including simulation accuracy, decision-making, and automation.

You’ll be part of a collaborative team, contributing to all phases of AI-driven software development—from requirements definition and design to coding, testing, and deployment. Our projects focus on applying AI and machine learning to solve complex aerospace challenges.

WHAT YOU’LL GET TO DO:

• Build AI-driven applications and integrate Large Language Models (LLMs) to enhance simulation accuracy, decision-making, and automation.
• Develop and deploy machine learning models using frameworks like TensorFlow or PyTorch.
• Collaborate with cross-functional teams to identify opportunities for AI integration into aerospace systems.
• Optimize AI applications for performance, scalability, and reliability in large-scale simulations and data processing.
• Conduct research and development to apply LLMs for aerospace-specific use cases, such as simulation, predictive modeling, or automated system configuration.
• Participate in design reviews (e.g., PDR, CDR) and contribute to the refinement of system requirements and technical specifications.
• Conduct code reviews to ensure adherence to company coding standards and best practices.

(Please note: This position can be hired at the Senior Software Engineer I or Senior Software Engineer II level.)

QUALIFICATIONS YOU’LL BRING AS SENIOR SOFTWARE ENGINEER I:

• Bachelor's degree in Computer Science, Computer Engineering, or a related technical discipline.
• 5+ years of professional experience in software development, with a focus on AI applications or machine learning.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and integrating ML models into software systems.
• Experience working with Large Language Models (LLMs) and AI applications for natural language processing, automation, or decision support.
• Proficiency in C#/.NET and Angular (or similar frontend frameworks like React or Vue).
• Strong understanding of relational databases (e.g., MySQL, SQL Server, SQLite).
• Solid grasp of object-oriented programming principles and design patterns.
• U.S citizenship is required, due to program requirements

QUALIFICATIONS YOU’LL BRING AS SENIOR SOFTWARE ENGINEER II:

• Bachelor's degree in Computer Science, Computer Engineering, or a related technical discipline.
• 8+ years of professional experience in software development, with a focus on AI applications or machine learning.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and integrating ML models into software systems.
• Experience working with Large Language Models (LLMs) and AI applications for natural language processing, automation, or decision support.
• Proficiency in C#/.NET and Angular (or similar frontend frameworks like React or Vue).
• Strong understanding of relational databases (e.g., MySQL, SQL Server, SQLite).
• Solid grasp of object-oriented programming principles and design patterns.
• U.S citizenship is required, due to program requirements

THESE QUALIFICATIONS WOULD BE NICE TO HAVE:

• Master's or PhD in Computer Science, Computer Engineering, or a related technical field.
• Experience with Docker/Kubernetes for containerization and orchestration.
• Knowledge of network programming (e.g., Sockets, WebSockets) for real-time data exchange.
• Familiarity with Git and modern version control workflows.
• Experience working in Agile/Scrum environments and contributing to iterative development cycles.
• Advanced knowledge of Angular NgRx or other state management libraries.
• Experience applying LLMs for aerospace-specific use cases, such as simulation, predictive modeling, or automated system configuration.
• Strong problem-solving skills and the ability to debug complex systems.
• A team-oriented mindset with excellent communication skills and a positive attitude.

ADDITIONAL REQUIREMENTS:

• Specific vision abilities required by this job include close vision, distance vision, peripheral vision, depth perception, and the ability to focus
• Regularly required to sit, use hands and fingers, operate computer keyboard and controls, and communicate verbally and in writing
• Must be physically able to commute to buildings 
• Occasional exposure to dust, fumes and moderate levels of noise

Position

Are you a passionate Software Engineer looking to bring cutting-edge software solutions to drug discovery? Join our mission to bring novel therapeutics to patients and be at the forefront of driving innovation through technology. As a Software Engineer 2, you will leverage your expertise to develop and implement software solutions that drive our groundbreaking scientific initiatives.

About You

The ideal candidate for this role thrives at the intersection of software and scientific discovery. You are proficient with production-quality Python codebases and can contribute to, maintain, and extend these codebases in a robust and scalable way. At the same time, you understand the fast pace of scientific discovery, and can collaborate with scientists to clarify requirements, focus on result rather than implementation, and keep projects moving forward. You are a strong communicator, especially to non-engineers, as well as a strong team player, always ready to pitch in to help a colleague. You are a continual learner with a growth mindset, eager to learn from your colleagues and expand your talents in a supportive environment and are excited about our mission to bring drugs to patients.

What You’ll Do

• Work with scientists to understand data generated by early drug discovery projects and provide custom analyses and/or production software solutions to answer key questions about the data.
• Build, maintain, and extend tools that deliver machine-learning-based as well as classical algorithmic solutions to scientists.
• Contribute to the continuous improvement of software development processes, tools, and methodologies.
• Conduct thorough code reviews, promoting best practices and identifying opportunities for code optimization and enhancement.
• Collaborate with team members, sharing insights and fostering a culture of knowledge sharing and learning.
• Uphold ethical and data privacy standards in all software engineering activities.

Qualifications

• BS in Computer Science, Mathematics, Physics, Bioinformatics, Biophysics, or a related field, plus 2 years of relevant experience, or equivalent work experience.
• Demonstrated experience (2+ years) with Python; familiarity with scientific programming packages (pandas, numpy) and/or machine learning frameworks (TensorFlow) a plus.
• Undergraduate level biology knowledge required; experience manipulating and analyzing biologically relevant data sets strongly desired (e.g. sequencing data, cellular imaging data, etc).
• Introductory coursework on deep learning or other modern ML approaches, or equivalent work experience, a plus.
• Familiarity with basic software best practices (CI/CDD, unit testing, version control, etc.) highly preferred.
• Strong analytical and problem-solving skills, with the ability to think critically and creatively to overcome technical challenges.
• Effective communication skills to collaborate with diverse teams, both technical and non-technical, especially across disciplines.
• Demonstrated ability to thrive in a fast-paced, dynamic work environment.

At Eikon, employee compensation also includes bonus and equity compensation, in addition to several generous benefit programs, including:​

• 401k plan with company matching​
• Medical (premiums covered by Eikon at 95%), dental and vision insurance (premiums covered by Eikon at 100%)​
• Mental health and wellness benefits​
• Weeklong summer and winter holiday shutdowns​
• Generous paid time off and holiday policies​
• Life/AD&D Insurance (premiums covered by Eikon at 100%) and optional supplemental employee-paid life/AD&D policies ​
• Enhanced parental leave benefit​
• Daily subsidized lunch program when on-site​

The expected salary range for this role is $135,000 to $147,250 depending on skills, competency, and the market demand for your expertise.

The Team

Our engineering team builds systems that interact with some of the most complex software  ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. 

We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Arize. While we are a remote-first company, the nature of this role requires candidates to be based in the San Francisco Bay area/PST time zone.

What You’ll Do

• Work closely with some of the most sophisticated ML / GenAI teams in the world.
  • You will act as a trusted advisor to our customers, while also building relationships with technical and business stakeholders.
• Advise on GenAI and ML best practices 
• Give ML and LLM product demos to technical and business stakeholders
• Run strategic business reviews for customers in partnership with our sales team
• Interface with our pre-sales engineering team to gather client goals and KPI’s.
• Partner with our product and engineering teams to help drive the product roadmap
• Spearhead new opportunities within existing accounts to help drive expansions.

What We’re Looking For

Note: Even if you do not check every single box, we still encourage you to apply! 

• Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production.
• Comfortable working in public Cloud environments (AWS, Azure, GCP)
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
• Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy
• Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
• Understanding of GenAI concepts and application evaluation + development lifecycle  
• Proficiency in a programming language (Python, JS/TS, Java, Go, etc)
• Strong Communication Skills - Ability to simplify complex, technical concepts.
• A quick and self learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV.

Bonus Points, But Not Required

• Previous engineering experience in:
  • Data Science
  • MLOps
  • ML Frameworks
  • LLM / Agentic frameworks
• Customer facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles
• Prior experience working with applications deployed with Kubernetes
• Prior experience demoing technical products to both business and technical audiences

The estimated annual salary and variable compensation for this role is between $125,000 - $175,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support.

While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces.

Are you looking to thrive in a stimulating work environment?  

Join Levio, a leader in digital transformation, and take your career to the next level. You will work alongside high-caliber professionals on ambitious, large-scale technology projects, directly embedded in our clients’ environments. At Levio, we value expertise, curiosity, and continuous improvement — and we give you the space to grow.  

About the Role

Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell® Fusion, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior. Auto insurers, automakers, commercial mobility companies, and the public sector use insights from CMT’s platform to power risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT measures and protects tens of millions of drivers across the world every day.

CMT is looking for a creative, collaborative, and highly motivated Machine Learning Intern to help develop applied machine learning capabilities using real-world mobility and behavioral data. This internship is ideal for someone who enjoys solving ambiguous problems, building practical ML workflows, working with messy real-world datasets, and translating technical findings into clear recommendations.

The intern will work on a 12-week applied ML project involving data exploration, labeling strategy, feature engineering, model development, evaluation, and stakeholder-facing analysis. The ideal candidate has strong Python skills, a solid foundation in machine learning, strong initiative, and the ability to make progress in open-ended problem spaces.

Responsibilities:

• Explore real-world mobility datasets and identify useful behavioral, temporal, contextual, and aggregate patterns
• Help define practical data labeling guidelines, quality criteria, and edge-case handling rules
• Build and validate datasets for supervised machine learning experiments
• Develop baseline and improved ML models using Python and standard data science workflows
• Perform feature engineering, model evaluation, error analysis, and iteration
• Assess model performance across relevant data segments and edge cases
• Summarize findings, limitations, risks, and opportunities for technical and product stakeholders
• Produce clear documentation, analysis reports, and final recommendations

Qualifications:

• Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, Machine Learning, Statistics, Engineering, Applied Math, or a related quantitative field
• Strong Python programming skills and comfort working with real-world datasets
• Solid understanding of supervised machine learning, feature engineering, classification metrics, train/test splits, and error analysis
• Experience with common Python data science libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or similar tools
• Strong analytical and problem-solving skills, with a bias toward initiative and ownership
• Clear written and verbal communication skills, including the ability to explain technical findings to non-technical stakeholders

Compensation and Benefits:

• Fair and competitive hourly rate based on education level
• Flexible scheduling options depending on role and responsibilities

Additional Perks:

• Feel great working to improve road safety around the world!
• Join one of our many employee resource groups including Black, AAPI, LGBTQIA+, Women, Book Club and Health & Wellness
• CMT will do all that is possible to support our employees and create a positive and inclusive work environment for all!

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.

Data and AI Analyst 

Start Your Career Where Innovation Meets Purpose 

At Accenture Federal Services, we help the U.S. federal government make the nation stronger, safer, and better for all. Our 13,000+ team members are united by a shared purpose: to harness the limitless potential of technology and human ingenuity for clients across defense, national security, public safety, civilian, and military health organizations.  

Join a collaborative and caring community where you belong, and where your growth is our priority. As an entry-level analyst, you’ll gain hands-on experience, earn industry certifications, and receive structured training that builds on your engineering mindset—applying creativity and analytical thinking to solve real-world challenges.  

We’re writing the playbook for how to be the most AI-enabled, client-focused, and people-first organization. Our reinventors are driving faster value for clients and shaping the future of government services.  

Are you ready to reinvent what’s possible?  

It’s your career, your life, and your choice. Let’s build something that matters—together.  

The Opportunity 

At AFS, our Data & AI Analysts help federal agencies unlock the power of data and artificial intelligence to drive smarter decisions, faster operations, and better outcomes for the nation. You’ll begin your journey with immersive training in data engineering, analytics, and AI integration—building the skills to solve complex challenges across defense, national security, and civilian sectors.   

In this role, you’ll apply your engineering mindset to design and implement data-driven solutions using modern tools, cloud platforms, and AI frameworks. You’ll work side-by-side with teammates to experiment with emerging technologies, adopt next-generation IT, and deliver mission-critical insights that move government forward.   

What You’ll Do: 

• Design, build, and maintain data pipelines and analytics platforms using tools like Python, SQL, Spark, and cloud-native services. 

• Apply machine learning and AI techniques to extract insights, automate processes, and support predictive decision-making. 

• Leverage frameworks such as Agentic AI to develop intelligent systems that adapt and respond to dynamic mission needs. 

• Visualize data and communicate findings using tools like Tableau, Power BI, and custom dashboards. 

• Collaborate with cross-functional teams to integrate AI models into operational workflows and ensure responsible, secure use of data. 

• Experiment with emerging technologies and next-gen architectures to accelerate solution delivery and scale impact. 

What You’ll Learn 

• AI engineering and model deployment using platforms like TensorFlow and PyTorch. 

• Data architecture, cloud analytics, and scalable data processing on AWS, Azure, or GCP. 

• Responsible AI practices, data governance, and ethical use of machine learning in federal environments. 

• Agile delivery, DevSecOps, and continuous learning through certifications and mentorship. 

Basic Qualifications 

• Bachelor’s or Master’s degree in a STEM or adjacent field (e.g., Computer Science, Statistics, Mathematics, Data Science), with graduation by August 31, 2026. 

• Engineering background with foundational skills in data analysis, programming, and problem-solving.  

• U.S. citizenship required (due to federal contract requirements) 

• Ability to travel 25-50% preferred, but requirements vary by team and location. 

Preferred Qualifications 

• Strong academic track record (3.0 cumulative GPA required, 3.2 cumulative GPA preferred) from a 4-year accredited institution.  

• Hands-on experience with Palantir Foundry and Databricks. 

• Experience with multiple programming languages, modern frameworks, cloud platforms, databases, and/or AI technologies. 

• Familiarity with data analysis and AI tools such as Python, R, SQL, Postgres, Tableau, Power BI, or machine learning platforms. 

• Ability to work collaboratively in cross-functional teams and communicate technical concepts clearly.  

• Active security clearance or eligibility to obtain one. 

Work Model 

Our people-first culture prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely.  

• If this position is listed as remote or hybrid, you’ll periodically work from an Accenture Federal or client site facility.  

• If this position is listed as onsite, you’ll work with colleagues and clients in person, as needed for a specific role.  

This is more than a job—it’s a launchpad for a technical career in federal innovation. Whether you aspire to be a cloud engineer, AI integration specialist, or software architect, we’ll support your growth with mentorship, certifications, and access to the latest technologies through the Analyst Development Program (ADP).  

Role Description:

We are looking for an independent, dedicated Machine Learning Engineer/Senior ML Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency.

What you’ll be doing:

• Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc.
• Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models
• Collaborate with team members on drafting publications and patent submissions
• Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment

What we’re looking for:

• PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post
• 3+ years of experience in demonstrable research experience in industry or academia.
• Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum of 3 years of experience for ML engineer, minimum 5 years for Senior ML engineer),
• Evidence of leading technical developments by at least 1 research publication in a top-tier conference like CVPR, ECCV, ICCV, SIGGRAPH or similar
• Solid background in one or more of the following:

(i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design;

(ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner;

(iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar

Desired qualifications:

• Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar
• Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing
• Experience in image processing theory and methods, evidenced by the development of practical designs in this area
• Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

Role Description:

We are looking for an independent, dedicated Senior ML Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D. VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency.

What you’ll be doing:

• Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc.
• Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models
• Collaborate with team members on drafting publications and patent submissions
• Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment

What we’re looking for:

• PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post
• 5+ years of experience in demonstrable research experience in industry or academia.
• Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum of 3 years of experience for ML engineer, minimum 5 years for Senior ML engineer),
• Evidence of leading technical developments by at least 1 research publication in a top-tier conference like CVPR, ECCV, ICCV, SIGGRAPH or similar
• Solid background in one or more of the following:

(i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design;

(ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner;

(iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar

Desired qualifications:

• Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar
• Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing
• Experience in image processing theory and methods, evidenced by the development of practical designs in this area
• Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. The CLV & Personalisation team focuses on understanding player behaviour and driving more effective engagement across the player lifecycle — from acquisition and onboarding through to retention, monetisation, and long-term value. 

As a Senior Data Scientist, you will drive the development of advanced modelling and analytical approaches that influence key commercial and product decisions at scale and are evaluated based on their impact on commercial outcomes. You will work on complex and often ambiguous problems, helping define how we measure player value and how we deliver more personalised player experiences. 

This role combines deep hands-on modelling with ownership of problem definition, approach, and impact. You will work with a high degree of autonomy across ambiguous problem spaces, shaping how data informs strategy and decision-making across the business. 

What You’ll Do 

• Design and deliver advanced machine learning models to drive player value, engagement, and growth across the lifecycle. 

• Own ambiguous problem spaces, defining modelling approaches and breaking down complex business challenges into actionable solutions. 

• Partner with senior stakeholders to shape strategies, and ensure Data Science drives high-impact decisions. 

• Apply advanced machine learning techniques, including deep learning and sequence modelling, where appropriate to capture complex player behaviours and improve model performance. 

• Evaluate and select modelling approaches based on problem context, balancing performance, scalability, and interpretability. 

• Work within cross-functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are scalable, production-ready, and aligned with long-term architecture. 

• Mentor other data scientists and contribute to best practices across the team. 

• Communicate complex modelling approaches, assumptions, and outcomes clearly to senior technical and non-technical audiences. 

What We’re Looking For 

You combine technical depth in machine learning with a proactive, solution-oriented mindset, and the ability to influence strategy and inspire others. You thrive on solving sophisticated problems that deepen PlayStation’s understanding of its players and you use this understanding to inform data-driven decisions and strategies that drive player growth. 

• Proven industry experience delivering impactful, production-ready machine learning solutions in areas such as customer value, retention, or personalisation. 

• Strong expertise in modern machine learning approaches, including applying techniques such as transformers, embeddings, or sequence-based models to real-world behavioural data. 

• Ability to critically assess when advanced approaches are warranted versus simpler solutions. 

• Experience owning projects end-to-end, including problem definition, navigating ambiguity, selecting modelling approaches, and delivering impactful solutions. 

• Ability to work with large, complex datasets and translate them into scalable solutions. 

• Strong stakeholder management and communication skills, with the ability to influence decisions and communicate results persuasively to both technical and non-technical audiences. 

• Proficiency in Python, SQL, and modern ML tooling (such as Tensorflow, PyTorch, MLlib, feature stores); experience with PySpark or equivalent distributed data processing tools. 

• A strong academic background, typically a Master’s or PhD in a technical or quantitative field, specialising in Machine Learning. 

• Experience deploying models into production and maintaining business-critical or consumer facing models. 

Nice to Have 

• Background in gaming, e-commerce, or subscription-based businesses. 

• Industry experience with experimentation frameworks and causal inference. 

• Experience working in agile, fast-paced environments 

About Wholesail ⛵️

Wholesale trade, a $55T global market, still operates like the world before the internet and modern payment networks: collection processes are manual and run on paper, short-term credit risk is assumed by the vendor, and businesses rely heavily on legacy ERP systems that don't talk to each other. The result is large, measurable drag — we estimate that vendors spend over $500B a year globally across industries on an inefficient mix of processing, bad debt, lending, credit insurance, software, and labor costs to get paid.

Wholesail is building a financial network from the ground up that connects the systems of vendors and buyers involved in wholesale trade to enable streamlined payment and the transfer of risk to third parties. This will allow vendors to offload risk and eliminate tens of billions of waste — while giving creditworthy buyers better terms and third-party capital, unlocking hundreds of billions (ultimately trillions) in additional sales. The primitives of this network scale across industries and geographies: a universal approach to ERP integrations, modern payment rails, and a live trade-credit bureau to underwrite risk — which we're calling Lighthouse.

The Opportunity: Risk & Capital Products 🚢

Credit is the load-bearing beam of our network. Every time a vendor ships goods before getting paid, someone is taking a risk — today it's the vendor, tomorrow it should be a third party at a fair price. Getting that transfer right is what unlocks the next order of magnitude of sales across the wholesale economy, and the only way to get it right is to underwrite buyers more accurately than anyone else in the industry. Listen to the Visa episode of the Acquired podcast to learn how credit card networks did this for retail trade. 

We think we're uniquely positioned to do this. Through Lighthouse, we're building a live, reciprocal trade-credit bureau: vendors on our network contribute real-time payment behavior on a long tail of SMB buyers that no traditional bureau sees. That data — combined with the bank, ERP, and transaction signals already flowing through Wholesail — is a modeling dataset that doesn't exist anywhere else. The first MLE on this team gets to decide what we build with it.

The problems are real and the stakes are significant. Our models directly shape the terms buyers are offered and the losses Wholesail and our capital partners absorb. There's no established playbook here and no legacy stack to inherit — you'll be setting the direction for how we do modeling, data engineering, and production ML at Wholesail for years to come.

The Role ⚒️

As the founding MLE on the Risk Engineering & Capital Products team, you'll own credit risk modeling end-to-end — from problem framing and data pipeline design, through model development and validation, through production serving and monitoring. You'll work directly with the hiring manager, our engineering and product leadership, and our hands-on advisor Dan Massoni (former Chief Commercial Credit Officer of American Express) to decide what to build, explore features to model and how you’ll scale our approach risk scoring. .

Expect your work to span:

• Modeling. Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing) against our proprietary reciprocal-bureau data and external signals. Choose the right tool for the job — gradient-boosted trees, deep models, LLMs, or classical statistical models — and defend the choice.
• Data engineering. Build the pipelines, feature logic, and training datasets that your models (and future models) run on. At this stage, the MLE owns the full data path; over time we'll grow a team around you.
• Productionization. Ship models into production systems where they make real decisions on real dollars. Own deployment, monitoring, drift detection, and iteration.
• Partnering cross-functionally. Work closely with product, engineering, capital markets, and external lending and insurance partners. Explain your models to people who care about the outcomes but not the math, and translate their constraints back into modeling decisions.
• Shaping the team. Help define the hiring bar and interview process for the data scientists and MLEs who will join after you. You'll have a strong voice in what this team becomes.

Qualifications

• 5+ years of experience building models for production use cases.
• Deep modeling skill: strong command of supervised learning on tabular data, including feature engineering, model selection, hyperparameter tuning, calibration, and rigorous offline and online evaluation.
• Strong data engineering skills — comfortable owning ETL and feature pipelines end-to-end against real, messy production data (SQL and a modern data-processing stack).
• Proficiency in Python and the modern ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM, Jupyter, etc.).
• Solid statistical reasoning — you can spot leakage, selection bias, label noise, and spurious correlations, and you know why your offline metric doesn't always predict online performance.
• Track record of owning models end-to-end: not just prototyping in a notebook, but taking something from a problem statement to a production system that makes decisions.
• Excellent written and spoken English communication skills; ability to explain model behavior, tradeoffs, and limitations to non-ML audiences (product, ops, capital partners, leadership).
• The spirit of a team player who believes in fostering a healthy and supportive work environment.

Bonus Qualifications

• Direct experience in credit risk modeling — PD/LGD/EAD models, scorecards, underwriting models, exposure management, or collections modeling — particularly for SMB or subprime segments.
• Experience in fintech, lending, payments, fraud, or insurance, or with regulated modeling environments (model risk management, adverse action, fair lending).
• Experience building or operating ML platform components — feature stores, model registries, training orchestration, online serving, monitoring/drift detection.
• Backend engineering depth: comfortable writing production services, designing APIs, and reasoning about distributed systems that consume your models.
• Strong data analysis background — fluency in exploratory analysis, experimentation, and translating data into product and business decisions.
• Experience with LLMs and agentic systems applied to operational problems (document understanding, KYB, entity resolution, agent tooling).
• Experience as the first or founding ML hire at a company, or early on a team that had to build ML infrastructure from scratch.
• Published research, Kaggle placements, or prominent OSS contributions in the ML or data ecosystem.
• BA or BS in Computer Science, Statistics, Mathematics, a related technical field, or equivalent practical experience.
• Advanced degree (MS or PhD) in a quantitative field.

Equal Employment Opportunity

Wholesail is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, pregnancy, or any other characteristic protected by federal, state, or local law. Employment decisions at Wholesail are based on qualifications, merit, and business needs. We encourage applicants from all backgrounds to apply.

• Help design, train, with a focus towards deploying onboard machine learning models that improve the performance/latency of the Kodiak autonomy stack which includes quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling.
• Help identify and achieve parity between onboard and deployed offboard models using validation on HIL 
• Collaborate with cross-functional teams including perception, planning, simulation, and autonomy infrastructure to integrate ML models into production systems.
• Analyze real-world driving logs to identify edge cases and improve model robustness and safety.
• Use profiling tooling to additionally help improve the train time for models by identifying and removing bottlenecks.
• Contribute to the development of scalable AI infrastructure that supports continuous learning and deployment across Kodiak’s fleet.

• MS or PhD in Computer Science, Robotics, Machine Learning, or a related technical field, or equivalent practical experience.
• 6+ years experience deploying machine learning or deep learning systems in production environments.
• Proficiency in Python, C++ and modern ML frameworks such as PyTorch or TensorFlow.
• Experience working with large-scale distributed training systems.
• Solid understanding of optimization, and machine learning fundamentals.
• Strong software engineering fundamentals including testing, debugging, and system design.
• Ability to work collaboratively across teams to solve complex autonomy and robotics challenges.

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits)
• MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
• Flexible PTO, 10 paid holidays, and generous parental leave policies
• Our office is centrally located in Mountain View, CA
• Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
• Long Term Disability, Short Term Disability, Life Insurance
• Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation) 
• Fidelity 401(k)
• Commuter, FSA, Dependent Care FSA, HSA
• Various incentive programs (referral bonuses, patent bonuses, etc.)

At scale, quantum computers will be integrated into classical datacenters — combining the unique strengths of CPUs, GPUs, and QPUs will help us solve the world’s hardest problems. Today, we already use IonQ quantum computers in tandem with high-performance computer (HPC) clusters in applications like quantum machine learning and image analysis. As a Senior Staff Software Engineer leading our HPC integration, you’ll help build and maintain our interfaces with SLURM and other open HPC stacks.

Responsibilities:

• Lead the design and development of our HPC software stack integrations
• Establish best practices for code development, quality assurance, documentation, and continuous integration and deployment
• Lead and participate in code reviews
• Effectively self-manage projects, priorities, deadlines, and deliverables; Identify, track and mitigate execution roadblocks and technical risks
• Mentor junior staff members; we’ll ultimately be building a team around you!
• Help minimize overhead between classical and quantum resources through efficient scheduling and parallelization

You’d be a good fit with:

• Bachelor’s degree in Computer Science, IT or equivalent practical experience -- untraditional backgrounds are welcome and encouraged!
• 13+ years experience in software engineering
• 5+ years experience of open source contributions
• 2+ years experience with Slurm or other HPC batch schedulers (LSF, PBS, GridEngine, etc.) and/or other HPC technologies
• Familiarity with containerization technologies such as Docker and container orchestration platforms like Kubernetes or Microk8s
• Familiarity with MPI or other multiprocessing schemes
• Experience with workflow orchestration tools (Argo Workflows, Kubeflow)
• Significant experience with system languages like C/C++ or Go, and advanced scripting in Python
• Established experience in Linux operating system, environment, and tools

You’d be a great fit with:

• 15+ years experience in software engineering
• 10+ years experience of making open source contributions  
• 5+ years in High Performance Compute cluster management
• 2+ years experience as an open source project maintainer with an active community
• Expert-level knowledge of Fortran and/or C
• Familiarity with quantum software stacks like MQSS or OpenQSE
• Familiarity with distributed computing frameworks like Ray or Dask
• Experience with GPU programming models such as CUDA (or even better: CUDA Quantum)
• Expert analysis skills in areas like statistical testing, modeling and general optimization
• Knowledge of one or more domains of computational physics (PDEs, n-body, large-dimension integration, stochastic systems, ...)
• Familiarity with common machine learning frameworks like PyTorch or TensorFlow
• Exposure to quantum physics and/or quantum information science

Location: This is a hybrid role based at our headquarters in College Park, MD or at our Bothell, Washington facility.
Travel: 10%
Job ID: 1135

The approximate base salary range for this position is $187,358- $245,300. The total compensation package includes base, bonus, and equity.

Lab Summary: AI Research Center (AIC) located in Mountain View, California focuses on research and development which directly impacts future Samsung products reaching hundreds of millions of users worldwide. We are focused on pushing the state-of-the-art and practice in natural language and knowledge intelligence. 

Position Summary: Samsung Research AI center, located in Mountain View, CA, is currently recruiting world-class students who can thrive in a fast-pace, cross team, results-driven environment, with focus on highly visible, challenging, and cross discipline projects. You will be part of an exciting project to build an adaptive, personalized, contextual and secure AI model and system to enable fast, accurate and safe interactions tailored to users’ needs on Samsung devices. 

Position Responsibilities:   We are looking for Fall Interns (Flexible start date for a 3 month internship between September-December).

• Develop and implement novel deep learning/reinforcement learning algorithms for natural language processing (text, speech) in various applications 
• Contribute to the research activities of our team 
• Generate creative solutions (patents) and publish in top conferences (papers) 

Required Skills:  

• Current Ph.D. student in CS, EE, or related field 
• Experience in one or more of the following areas:  
  • Expertise in LLM including model architecture, training/finetuning techniques, retrieval augmented generation (RAG), reasoning and action planning, etc.
  • Experience in planning, tool use, agent AI, and agent memory to develop autonomous systems for decision-making, problem-solving, and adaptability. 
  • Experience in knowledge augmented AI technologies (e.g., language prompt, knowledge graph, neuro-symbolic learning)
  • Experience in conversational AI technologies: natural language processing (e.g., language models, semantic parsing, natural language generation etc.), dialogue (e.g., state tracking, policy learning), and representation learning (embedding, conceptualization, etc.)
  • Experience in multimodal AI technologies for various multimodal applications
  • Experience in on-device AI technologies such as lightweight model architecture design
• Teamwork and communication skills 
• Proficiency in a neural network library (e.g., PyTorch, TensorFlow)
• Track record of research/publications on machine learning and artificial intelligence field (NeurIPS, ICML, ICLR, AAAI, IJCAI, CVPR, ACL, EMNLP, NAACL, TACL, etc.) 

The opportunity
Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company.

Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform enables large-scale model training, feature generation, and experimentation workflows that power production ML systems.

We’re looking for a Machine Learning Engineer to join our Offline Infrastructure team. This is an ideal role for a recent PhD graduate who is excited to work on large-scale systems and apply research-driven thinking to real-world machine learning problems.

You’ll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training. Working closely with experienced engineers and researchers, you’ll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient.

This role offers the opportunity to bridge research and production—translating advanced ideas into systems that operate at scale.

What you'll be doing

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
• Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray)
• Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines
• Improve reproducibility and reliability through dataset validation, monitoring, and testing
• Partner with ML engineers to support experimentation and model iteration
• Help optimize performance and efficiency across data processing and training systems
• Contribute to the evolution of our offline ML platform architecture as it scales

What we're looking for

• PhD in Computer Science, Machine Learning, Systems, or a related field
• Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects)
• Experience with Python and working with data-intensive workloads
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark)
• Experience (academic or applied) with data pipelines, model training workflows, or large datasets
• Strong problem-solving skills and ability to translate research ideas into practical systems
• Interest in building scalable, reliable infrastructure for machine learning
• Nice to Have
• Experience with workflow orchestration systems (Airflow, Flyte, etc.)
• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)
• Publications or research in ML systems, distributed systems, or related areas

Additional information

• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this Web site or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#MID #LI-ED1

The opportunity
Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company.

Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform enables large-scale model training, feature generation, and experimentation workflows that power production ML systems.

We’re looking for a Machine Learning Engineer to join our Offline Infrastructure team. This is an ideal role for a recent PhD graduate who is excited to work on large-scale systems and apply research-driven thinking to real-world machine learning problems.

You’ll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training. Working closely with experienced engineers and researchers, you’ll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient.

This role offers the opportunity to bridge research and production—translating advanced ideas into systems that operate at scale.

What you'll be doing

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
• Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray)
• Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines
• Improve reproducibility and reliability through dataset validation, monitoring, and testing
• Partner with ML engineers to support experimentation and model iteration
• Help optimize performance and efficiency across data processing and training systems
• Contribute to the evolution of our offline ML platform architecture as it scales

What we're looking for

• PhD in Computer Science, Machine Learning, Systems, or a related field
• Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects)
• Experience with Python and working with data-intensive workloads
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark)
• Experience (academic or applied) with data pipelines, model training workflows, or large datasets
• Strong problem-solving skills and ability to translate research ideas into practical systems
• Interest in building scalable, reliable infrastructure for machine learning
• Nice to Have
• Experience with workflow orchestration systems (Airflow, Flyte, etc.)
• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)
• Publications or research in ML systems, distributed systems, or related areas

Additional information

• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#MID #LI-ED1

The opportunity
Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company.

Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform enables large-scale model training, feature generation, and experimentation workflows that power production ML systems.

We’re looking for a Machine Learning Engineer to join our Offline Infrastructure team. This is an ideal role for a recent PhD graduate who is excited to work on large-scale systems and apply research-driven thinking to real-world machine learning problems.

You’ll help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training. Working closely with experienced engineers and researchers, you’ll contribute to systems that ensure our ML pipelines are reliable, scalable, and efficient.

This role offers the opportunity to bridge research and production—translating advanced ideas into systems that operate at scale.

What you'll be doing

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation
• Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray)
• Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines
• Improve reproducibility and reliability through dataset validation, monitoring, and testing
• Partner with ML engineers to support experimentation and model iteration
• Help optimize performance and efficiency across data processing and training systems
• Contribute to the evolution of our offline ML platform architecture as it scales

What we're looking for

• PhD in Computer Science, Machine Learning, Systems, or a related field
• Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects)
• Experience with Python and working with data-intensive workloads
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark)
• Experience (academic or applied) with data pipelines, model training workflows, or large datasets
• Strong problem-solving skills and ability to translate research ideas into practical systems
• Interest in building scalable, reliable infrastructure for machine learning
• Nice to Have
• Experience with workflow orchestration systems (Airflow, Flyte, etc.)
• Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)
• Publications or research in ML systems, distributed systems, or related areas

Additional information

• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#MID #LI-ED1

 Who We Are

2K is headquartered in Novato, California and is a wholly owned label of Take-Two Interactive Software, Inc. (NASDAQ: TTWO). Founded in 2005, 2K Games is a global video game company, publishing titles developed by some of the most influential game development studios in the world. Our studios responsible for developing 2K’s portfolio of world-class games across multiple platforms, include Visual Concepts, Firaxis, Hangar 13, CatDaddy, Cloud Chamber, 31st Union, HB Studios, and 2K SportsLab. Our portfolio of titles is expanding due to our global strategic plan, building and acquiring exciting studios whose content continues to inspire all of us! 2K publishes titles in today’s most popular gaming genres, including sports, shooters, action, role-playing, strategy, casual, and family entertainment.

Our team of engineers, marketers, artists, writers, data scientists, producers, thinkers and doers, are the professional publishing stewards of 2K’s portfolio currently includes several AAA, sports and entertainment brands, including global powerhouse NBA®️ 2K,  renowned BioShock®️, Borderlands®️, Mafia, Sid Meier’s Civilization®️ and XCOM®️ brands; popular WWE®️ 2K and WWE®️ SuperCard franchises, TopSpin 2K25, as well as the critically and commercially acclaimed PGA TOUR®️ 2K.

At 2K, we pride ourselves on creating an inclusive work environment, which means encouraging our teams to Come as You Are and do your best work! We encourage ALL applicants to explore our global positions, even if they don’t meet every requirement for the role.  If you're interested in the job and think you have what it takes to work at 2K, we encourage you to apply!

 What You Will Do

 2K Games is building a world-class data ecosystem to power real-time analytics, machine learning, and game intelligence across all our franchises. We are looking for a Senior Product Manager, Data Science to drive the vision, strategy, and execution of our data science strategies and products, partnering with game studios and analytics teams to enable enhanced player experiences.

This role requires a blend of strategic development, product management expertise, and technical understanding of data science and machine learning (ML) methodologies. The ideal candidate will seamlessly integrate data-driven strategies with player-centric goals, ensuring AI and analytics improve game design, live operations, and business intelligence.

Strategic Vision & Product Leadership

• Develop and implement the product strategy for 2K’s central data science platform, partnering with Data Scientists across our gaming business units to drive predictive analytics, player personalization, and real-time decision-making;
• Collaborate with the Director of Product, 2K Data to align the data engineering roadmap with customer objectives and player-centric outcomes;
• Define and prioritize a high-impact data science roadmap to lead a team of central data scientists in driving a variety of use cases such as player retention modeling, dynamic content generation, and real-time fraud detection. 

Data Science Platform Development

• Lead the development of reusable and extensible machine learning models, feature engineering pipelines, and ML-driven insights using tools like Databricks, MLflow, and SageMaker;
• Ensure data science initiatives integrate seamlessly with the real-time data infrastructure, supporting streaming analytics, AI-powered LiveOps, and dynamic matchmaking systems;
• Establish and maintain self-service tools that enable game teams to experiment with and deploy AI models independently;
• Support the build and enhancement of the central ML feature store;
• Monitor and optimize model performance in production environments, using MLOps standard methodologies;
• Implement governance frameworks to ensure AI models adhere responsibly to data privacy, security, and compliance standards (e.g., CCPA, GDPR). 

Cross-Functional Collaboration

• Work closely with data scientists across game studios and business units to understand their data science needs and to provide data-driven solutions at the pace they need them;
• Act as a strategic liaison between data scientists and product teams, ensuring model outputs are actionable, reliable, and aligned with business goals;
• Promote a culture of experimentation and rapid iteration, supporting game studios with A/B testing frameworks, predictive analytics, and personalization engines.
• Find opportunities to enhance game design and player engagement through advanced analytics and machine learning.

 What You Need

• 7+ years of experience in product management with a focus on data science, analytics, or ML-driven products
• 5+ years of experience in gaming, technology, or entertainment industries;
• Strong understanding of data science methodologies, such as predictive modeling, natural language processing, and computer vision;
• Experience with data science tools and platforms, such as Python, SQL, Databricks,TensorFlow, and R;
• Customer-focused mentality with focus on the Why and What without getting lost in the What and the How;
• Consistent track record of delivering data science products and features from ideation to production, with measurable business impact;
• Ability to translate complex data insights into clear product strategies and business opportunities;
• Knowledge of real-time analytics platforms, streaming data processing, and feature store management;
• Familiarity with data governance frameworks, model risk management, and MLOps standard processes preferred;
• Advanced degree or equivalent experience in Data Science, Machine Learning, Computer Science, or a related field preferred.

The pay range for this position in California at the start of employment is expected to be between $109,800 and $162,480 per Year. However, base pay offered is based on market location, and may vary further depending on individualized factors for job candidates, such as job-related knowledge, skills, experience, and other objective business considerations. Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or equity awards and eligibility to participate in our 401(K) plan and Employee Stock Purchase Program. Regular, full-time employees are also eligible for a range of benefits at the Company, including: medical, dental, vision, and basic life insurance coverage; 14 paid holidays per calendar year; paid vacation time per calendar year (ranging from 15 to 25 days) or eligibility to participate in the Company’s discretionary time off program; up to 10 paid sick days per calendar year; paid parental and compassionate leave; wellbeing programs for mental health and other wellness support; family planning support through Maven; commuter benefits; and reimbursements for fitness-related expenses.

As an equal opportunity employer, we are committed to ensuring that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform their essential job functions, and to receive other benefits and privileges of employment. Please contact us if you need reasonable accommodation.

Please note that 2K Games and its studios never uses instant messaging apps or personal email accounts to contact prospective employees or conduct interviews and when emailing, only use 2K.com accounts. 

#LI-Onsite

The Team

Our engineering team builds systems that interact with some of the most complex software  ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. 

We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Arize.

What You’ll Do

• Work closely with some of the most sophisticated ML / GenAI teams in the world.
  • You will act as a trusted advisor to our customers, while also building relationships with technical and business stakeholders.
• Advise on GenAI and ML best practices 
• Give ML and LLM product demos to technical and business stakeholders
• Run strategic business reviews for customers in partnership with our sales team
• Interface with our pre-sales engineering team to gather client goals and KPI’s.
• Partner with our product and engineering teams to help drive the product roadmap
• Spearhead new opportunities within existing accounts to help drive expansions.

What We’re Looking For

Note: Even if you do not check every single box, we still encourage you to apply! 

• Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production.
• Comfortable working in public Cloud environments (AWS, Azure, GCP)
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
• Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy
• Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
• Understanding of GenAI concepts and application evaluation + development lifecycle  
• Proficiency in a programming language (Python, JS/TS, Java, Go, etc)
• Strong Communication Skills - Ability to simplify complex, technical concepts.
• A quick and self learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV.

Bonus Points, But Not Required

• Previous engineering experience in:
  • Data Science
  • MLOps
  • ML Frameworks
  • LLM / Agentic frameworks
• Customer facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles
• Prior experience working with applications deployed with Kubernetes
• Prior experience demoing technical products to both business and technical audiences

Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes unlimited paid time off, generous parental leave plan, and others for mental and wellness support.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank is seeking a VP-level Applied AI & Machine Learning Scientist to design, build, and validate production-grade AI/ML and Generative AI solutions in a highly regulated banking environment. This role focuses on high-impact use cases—fraud detection, AML alert optimization, AI-assisted credit memo generation for underwriting decision support, contact center AI assistant/copilots, and personalization for treasury/commercial clients—delivered with rigorous governance, explainability, fairness testing, privacy-by-design, cybersecurity, and model lifecycle controls aligned to SR 11-7 and MCB’s Trustworthy & Responsible AI Principles. The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Applied AI/ML development:

• Design and implement models for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting and underwriting decision support, contact center AI assistants, and personalization for commercial/treasury use cases.
• Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.
• Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre-/post-deployment bias testing with documented remediation.

Model validation, documentation & governance (SR 11‑7):

• Produce audit-ready documentation (methodology, assumptions, data lineage, limitations, testing) and register models in the inventory with owners/materiality.
• Facilitate independent validation/effective challenge; obtain required approvals before deployment; maintain change management and periodic review cadence.
• Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.

Productionization & MLOps on Snowflake

• Package, deploy, and operate models via CI/CD, containerization, and model registry; instrument KPIs/KRIs and alerting dashboards. Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
• Partner with Engineering to integrate models via secure APIs/batch; ensure scalability, resiliency, and observability in cloud/on‑prem (e.g., Snowflake, Azure ML, Databricks).

Regulatory, privacy, and cybersecurity alignment:

• Design for ECOA/Reg B (adverse action specificity), UDAAP, FCRA, GLBA privacy, and NYDFS 23 NYCRR 500 cybersecurity requirements.
• Apply privacy-by-design (data minimization, purpose limitation, retention), strong access controls/segregation, and secure SDLC/red teaming for GenAI stacks.

Third‑party AI & data stewardship:

• Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
• Negotiate/verify contractual controls (no vendor training on MCB/NPI, subprocessors disclosure, audit rights, exit/portability).
• Ensure AEDT compliance (NYC Local Law 144) for any HR-related AI tools.

Cross‑functional partnership:

• Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.
• Communicate complex results, risks, and limitations clearly to technical and non‑technical stakeholders (management committees, examiners).

Innovation, coaching, and best practices:

• Evaluate emerging ML/GenAI methods, LLM evaluation techniques, Snowflake‑native capabilities (e.g., vector search, orchestration), and governance tooling; lead POCs within established control gates.
• Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.

Qualifications & Skills:

• 6+ years of relevant work experience.
• Expertise in Python (pandas, scikit‑learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• MLOps proficiency with CI/CD, containerization (Docker), registries, and observability; cloud ML (Snowflakes-native ML, Azure ML or Databricks preferred).
• Snowflake‑native ML proficiency: Snowpark Python, UDFs/UDTFs, Tasks/Streams; ability to build and operate ML workflows inside Snowflake.
• Data engineering competency (SQL, ETL/pipelines, Spark/PySpark); ability to work with structured/unstructured data.
• Explainability (e.g., SHAP) and fairness testing; ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
• Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
• Excellent communication; ability to translate technical detail to business/risk stakeholders and drive decisions.
• Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.

Preferred Qualifications & Skills

• Master’s or PhD in a relevant field (Computer Science, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML, NLP, or model interpretability.
• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions; experience with feature management/registry tooling a plus. model registry and pipeline orchestration; Kubernetes a plus.
• RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).
• Fairness toolkits and XAI frameworks; experience preparing models for validation, audit, or regulatory exam discussions.
• Familiarity with SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment
• Must have excellent written and verbal communication skills
• Must be a good listener and good teacher
• Demonstrate analytical, troubleshooting and problem-solving skills
• The ability to learn new technologies quickly
• Self-directed individual with technology and communication skills.
• Ability to take in multiple sources of information with an understanding of the bigger picture need, want, and operation of the Bank.
• Collaborative team-player who can find creative and practical solutions in a dynamic work environment.
• Ability to handle ambiguity, juggle multiple matters at once, and quickly and seamlessly shift from one situation or task to another.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Production Architecture & AIOps

• Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
• Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
• Design and maintain scalable software applications with integrated AI/ML capabilities.
• Develop software architecture and design patterns to ensure performance and scalability.
• Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
• Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
• Integrate AI/ML models into production environments and optimize for reliability and scalability.
• Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
• Conduct code reviews and provide technical guidance to junior developers.
• Stay current with advancements in software engineering and AI/ML technologies.
• Adhere to agile and lean software development best practices.
• Thoroughly document all developed models and processes according to relevant policies and standards.
• Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.

Cross-Functional Collaboration

• Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
• Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.

Scaling & Performance

• Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
• Optimize model serving using techniques like model compression, caching, and hardware acceleration.

Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).

Qualifications & Skills:

• 6+ years of experience
• Master’s or PhD in a relevant field (Computer Science, Software Engineering,  Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
• LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
• MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
• Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
• Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
• Strong understanding of AI/ML algorithms, application architecture, and design patterns.
• Excellent problem-solving, analytical, communication, and collaboration skills.

Preferred Qualifications & Skills

• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
• Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
• Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment.
• Excellent written and verbal communication skills.
• Strong listening and teaching abilities.
• Demonstrated analytical, troubleshooting, and problem-solving skills.
• Quick learner of new technologies.
• Self-directed with strong technology and communication skills.
• Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
• Collaborative team player with a practical and creative approach in a dynamic work environment.
• Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

PLEASE NOTE: This is a fully remote, 1099 independent contractor opportunity with an estimated duration of six months and the potential for extension. To be eligible, candidates must be authorized to work in the United States; visa sponsorship is not available for this role.

About the Program

The Human Frontier Collective (HFC) Fellowship brings together top researchers and domain experts to collaborate on high-impact work that are shaping the future of AI. As an HFC Fellow, you’ll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems—while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.

What You'll Do

• ML Projects: Get invited to engage in high-impact projects with our partnered AI labs and platforms. Help models understand real-world deep learning workflows by designing, reviewing, and optimizing PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimization, scaling, and trade-offs.
• HFC Community: Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.
• Contribute to Research Publications: Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., SciPredict, PropensityBench, Professional Reasoning Benchmark).

Who Should Apply

• Education: PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.
• Professional Background: 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.
• Skills: Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.
• Professional Mindset: Detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration.

Why Join the HFC?

• Professional Development: High-impact experts expand their influence through review projects, advisory roles, and research, while deepening their AI expertise, strengthening analytical and problem-solving skills, and engaging with pioneering AI applications in science and technology.
• Join a Top-Tier Network: Collaborate with a global network of engineers and experts to advance responsible AI through impactful, flexible research and training. 80% of our members come from leading institutions.
• Flexible Schedule: Set your own schedule, with flexible 10–40 hour weeks that fit around your life and other commitments.
• Competitive Pay: Project pay rates vary across platforms and are depending on a number of factors, including but not limited to; projects, scope, skillset, and location. 

Application Process

1. Apply: We review applications on a rolling basis.
2. Interview: Candidates will get to discuss their research experience, professional background, and alignment with our mission to advance human-centered AI.
3. Join the Collective: Successful candidates will receive an invitation to join the Human Frontier Collective Fellowship.

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and  unique access to massive datasets to deliver improvements to our customers.

Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.

You will:

• Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
• Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
• Work with massive datasets to develop both generic models as well as fine tune models for specific products
• Build scalable machine learning infrastructure to automate and optimize our ML services
• Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
• Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
• Comfortable with light travel (approximately 10%) for customer interaction and team needs
• This role will require an active security clearance or the ability to obtain a security clearance

Ideally You’d Have:

• Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
• Solid background in algorithms, data structures, and object-oriented programming
• Strong programing skills in Python, experience in Tensorflow or PyTorch

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
• Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
• Experience with computer vision, generative AI models, large language models, or agentic systems
• Familiarity with ML evaluation frameworks and agentic model design

Senior Machine Learning Engineer - Model Evaluations, Public Sector

The Public Sector ML team at Scale deploys advanced AI systems—including LLMs, agentic models, and multimodal pipelines—into mission-critical government environments. We build evaluation frameworks that ensure these models operate reliably, safely, and effectively under real-world constraints. As an ML Engineer, you will design, implement, and scale automated evaluation pipelines that help customers trust and operationalize advanced AI systems across defense, intelligence, and federal missions.

You will:

• Develop and maintain automated evaluation pipelines for ML models across functional, performance, robustness, and safety metrics, including LLM-judge–based evaluations.
• Design test datasets and benchmarks to measure generalization, bias, explainability, and failure modes.
• Build evaluation frameworks for LLM agents, including infrastructure for scenario-based and environment-based testing.
• Conduct comparative analyses of model architectures, training procedures, and evaluation outcomes.
• Implement tools for continuous monitoring, regression testing, and quality assurance for ML systems.
• Design and execute stress tests and red-teaming workflows to uncover vulnerabilities and edge cases.
• Collaborate with operations teams and subject matter experts to produce high-quality evaluation datasets.
• Comfortable with light travel (approximately 10%) for customer interaction and team needs.

This role will require an active security clearance or the ability to obtain a security clearance.

Ideally you’d have:

• Experience in computer vision, deep learning, reinforcement learning, or NLP in production settings.
• Strong programming skills in Python; experience with TensorFlow or PyTorch.
• Background in algorithms, data structures, and object-oriented programming.
• Experience with LLM pipelines, simulation environments, or automated evaluation systems.
• Ability to convert research insights into measurable evaluation criteria.

Nice to haves:

• Graduate degree in CS, ML, or AI.
• Cloud experience (AWS, GCP) and model deployment experience.
• Experience with LLM evaluation, CV robustness, or RL validation.
• Knowledge of interpretability, adversarial robustness, or AI safety frameworks.
• Familiarity with ML evaluation frameworks and agentic model design.
• Experience in regulated, classified, or mission-critical ML domains.

PLEASE NOTE: This is a fully remote, 1099 independent contractor opportunity with an estimated duration of six months and the potential for extension. To be eligible, candidates must be authorized to work in Canada.

About the Program

The Human Frontier Collective (HFC) Fellowship brings together top researchers and domain experts to collaborate on high-impact work that are shaping the future of AI. As an HFC Fellow, you’ll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems—while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.

What You'll Do

• ML Projects: Get invited to engage in high-impact projects with our partnered AI labs and platforms. Help models understand real-world deep learning workflows by designing, reviewing, and optimizing PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimization, scaling, and trade-offs.
• HFC Community: Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.
• Contribute to Research Publications: Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., SciPredict, PropensityBench, Professional Reasoning Benchmark).

Who Should Apply

• Education: PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.
• Professional Background: 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.
• Skills: Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.
• Professional Mindset: Detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration.

Why Join the HFC?

• Professional Development: High-impact experts expand their influence through review projects, advisory roles, and research, while deepening their AI expertise, strengthening analytical and problem-solving skills, and engaging with pioneering AI applications in science and technology.
• Join a Top-Tier Network: Collaborate with a global network of engineers and experts to advance responsible AI through impactful, flexible research and training. 80% of our members come from leading institutions.
• Flexible Schedule: Set your own schedule, with flexible 10–40 hour weeks that fit around your life and other commitments.
• Competitive Pay: Project pay rates vary across platforms and are depending on a number of factors, including but not limited to; projects, scope, skillset, and location. 

Application Process

1. Apply: We review applications on a rolling basis.
2. Interview: Candidates will get to discuss their research experience, professional background, and alignment with our mission to advance human-centered AI.
3. Join the Collective: Successful candidates will receive an invitation to join the Human Frontier Collective Fellowship.

PLEASE NOTE: This is a fully remote, 1099 independent contractor opportunity with an estimated duration of six months and the potential for extension. To be eligible, candidates must be authorized to work in the country they reside in.

About the Program

The Human Frontier Collective (HFC) Fellowship brings together top researchers and domain experts to collaborate on high-impact work that are shaping the future of AI. As an HFC Fellow, you’ll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems—while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.

What You'll Do

• ML Projects: Get invited to engage in high-impact projects with our partnered AI labs and platforms. Help models understand real-world deep learning workflows by designing, reviewing, and optimizing PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimization, scaling, and trade-offs.
• HFC Community: Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.
• Contribute to Research Publications: Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., SciPredict, PropensityBench, Professional Reasoning Benchmark).

Who Should Apply

• Education: PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field.
• Professional Background: 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.
• Skills: Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus.
• Professional Mindset: Detail-oriented, innovative thinker with a passion in applied AI research and a commitment to collaboration.

Why Join the HFC?

• Professional Development: High-impact experts expand their influence through review projects, advisory roles, and research, while deepening their AI expertise, strengthening analytical and problem-solving skills, and engaging with pioneering AI applications in science and technology.
• Join a Top-Tier Network: Collaborate with a global network of engineers and experts to advance responsible AI through impactful, flexible research and training. 80% of our members come from leading institutions.
• Flexible Schedule: Set your own schedule, with flexible 10–40 hour weeks that fit around your life and other commitments.
• Competitive Pay: Project pay rates vary across platforms and are depending on a number of factors, including but not limited to; projects, scope, skillset, and location. 

Application Process

1. Apply: We review applications on a rolling basis.
2. Interview: Candidates will get to discuss their research experience, professional background, and alignment with our mission to advance human-centered AI.
3. Join the Collective: Successful candidates will receive an invitation to join the Human Frontier Collective Fellowship.

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About the ACE team 

The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. 

About This Role

This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Ideally you’d have: 

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.
• Strong written and verbal communication skills and the ability to operate cross-functionally. 

Nice to have:

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax. 
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH 
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, fueling the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re amplifying access to high-quality data to drive progress toward Artificial General Intelligence (AGI). Building on our history of model evaluation with enterprise and government customers, we are expanding our capabilities to set new standards for both public and private evaluations.

About This Role

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale’s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Ideally, you’d have:

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow. You should also be skilled at rapidly interpreting research literature and turning new ideas into working prototypes.

• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.).

• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning.

• Strong written and verbal communication skills, along with the ability to work effectively across teams.

Nice to have:

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX.

• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents.

• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar.

• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH.

• Proficiency in cloud-based ML development, with experience in AWS or GCP environments.

Our research interviews are designed to assess candidates' ability to prototype and debug ML models, their depth of understanding in research concepts, and their alignment with our organizational culture. We do not conduct LeetCode-style problem-solving assessments.

About This Role

Lead applied ML engineering on Scale's Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviors, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes.

Ideal for exceptional engineers with deep research rigor and a relentless focus on practical, high-impact systems. You will iterate rapidly with data, leverage AI tools to accelerate development, and collaborate tightly across engineering, product, and research.

If you excel at turning frontier agent research into reliable deployed systems, we want to hear from you.

You will:

• Build and deploy multi-agent systems for agentic reasoning validation
• Develop pipelines to detect errors and scale human judgment
• Combine classical ML, LLMs, and multi-agent techniques for reliability
• Lead research into agent failure modes and ship fixes
• Use AI tools to speed prototyping and iteration
• Build data-driven evaluations and deploy rapid improvements
• Integrate systems into Scale's platform

Ideally You’ll Have: 

• PhD or MSc in Computer Science, Mathematics, Statistics, or related field
• 3+ years shipping scaled production ML systems
• Demonstrated real-world impact
• Mastery of PyTorch, TensorFlow, JAX, or scikit-learn
• Deep expertise in agentic LLMs and multi-agent systems
• Strong software engineering and microservices (AWS/GCP)
• Rapid, data-driven iteration
• Proficiency using AI tools to accelerate work
• Strong research depth with practical bias
• Excellent cross-functional communication

Nice to Have: 

• Experience prototyping agent evaluation/reliability systems
• Human-in-the-loop or annotation pipeline work
• Open-source contributions in agents, evaluation, or alignment
• Publications on agent reliability (NeurIPS, ICML, ICLR)

Role 

We are looking for an experienced Staff Machine Learning Engineer to join our Engineering team. This role is based in Bangalore, reporting to the Senior Engg. Manager - Data + AI.

You will be responsible for designing and developing large scale ML Systems that analyze massive volumes of network data to detect anomalies, threats, and patterns in near real-time. You’ll solve complex real-world business problems by partnering closely with data architects and product teams to deliver impactful automation and intelligence, including the design and orchestration of specialized autonomous agents.

What you’ll do (Role Expectations)

• Build and maintain large-scale distributed systems to support the full ML lifecycle, including data collection, feature engineering, and Agentic AI components like agent workflows and real-time serving
• Solve complex business problems such as threat detection by collaborating with data architects and product teams to build multi-agent solutions that automate analysis and decisioning
• Partner with cross-functional teams to assess requirements, manage scheduling, and lead the development and debugging of agent-based systems
• Support the rollout and monitoring plans for production-grade ML systems to ensure performance, observability, and scalability

Who You Are (Success Profile)

• You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
• You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
• You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
• You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

What We’re Looking for (Minimum Qualifications)

• 5+ years of experience as an AI-ML Engineer with technical proficiency in Java, Springboot, Python, and PyTorch or TensorFlow
• Proven experience writing high-performance queries for large-scale analytics platforms such as Azure Data Explorer (ADX/Kusto) or Google BigQuery, including partitioning-aware patterns
• Deep expertise in designing and deploying anomaly detection using tree-based models, statistical techniques, and streaming ML approaches
• Hands-on experience developing multi-agent orchestration, agent-to-agent communications, and autonomous decision-making workflows
• Strong technical background with distributed infrastructure and public cloud platforms like Azure, GCP, or AWS

What Will Make You Stand Out (Preferred Qualifications)

• A track record of shipping end-to-end applications at scale that process billions of events
• Willingness to work across the full spectrum of Big Data and AI-based solutions
• Hands-on experience with SOTA AI frameworks, prompt engineering, and complex agent architectures

#LI-Bangalore #LI-AN4

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology- and data-driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high-quality returns for our investors.

You will work on the HPC Research Platform, building and evolving an in-house research platform that provides efficient, secure access to compute and data for research teams across the organisation. The focus is on enabling the execution of large-scale compute pipelines by developing the services and tools that underpin job submission, scheduling, security, and observability across a range of environments. You will collaborate closely with Quantitative Researchers as well as engineering and infrastructure stakeholders to deliver a platform that supports computational research at scale.

Your Future Role Within QRT 

• Collaborate closely with quantitative researchers, platform engineers, and other cross-functional teams to understand research workflows and translate them into scalable, reliable platform capabilities 

• Design, build, and maintain distributed services and APIs that manage compute workloads, data access, and execution lifecycles across HPC environments 

• Improve the usability, reliability, and performance of large-scale quantitative research workflows, with a strong focus on developer experience and operational excellence 

• Contribute to the design and implementation of scheduling, security, and observability solutions suitable for highly parallel, compute-heavy systems 

• Work with state-of-the-art infrastructure and modern software engineering practices to support massive compute pipelines and rapidly evolving research demands 

• Take ownership of complex technical problems, operating with a high degree of autonomy while making pragmatic trade-offs to deliver impact quickly and safely 

Your Present Skill Set 

• BSc / MEng / PhD in Computer Science or a closely related field, or equivalent practical experience, with 5+ years of professional software development 

• Strong proficiency in at least one statically-typed programming language (e.g. C, C++, Java, Rust), with an interest in Go (prior experience welcome but not required). Experience with Python is a plus 

• Required: 5+ years of experience designing, implementing, and operating large-scale distributed systems in production environments 

• Preferred: Prior experience in quantitative finance or working closely with quantitative researchers and data-intensive workloads 

• Preferred Understanding of GPU programming models and performance considerations (e.g. CUDA, memory hierarchies, parallel execution), with exposure to ML frameworks such as PyTorch or TensorFlow 

• Solid understanding of systems concepts such as concurrency, performance, fault tolerance, and reliability 

• Comfortable working in fast-moving, rapidly evolving environments; proactive in learning new technologies and improving existing systems 

• Pragmatic engineering mindset, with a focus on delivering high-impact solutions while maintaining long-term platform health 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

We are seeking a Software Engineer to join our HPC Research Infrastructure team, responsible for building and evolving an in-house research platform that underpins quantitative research across the firm. The platform provides scalable, secure, and efficient access to high-performance compute and data, enabling researchers to run large-scale, compute-intensive pipelines with minimal friction. 

In this role, you will design and implement core platform components including APIs, backend services, and tooling to support job submission, scheduling, resource management, security, and observability across heterogeneous compute environments (on-prem and/or cloud). You will operate at the intersection of software engineering, distributed systems, and infrastructure, helping to deliver a robust, developer-friendly platform that enables research at scale and has company-wide impact. 

Responsibilities

• Collaborate closely with quantitative researchers, platform engineers, and other cross-functional teams to understand research workflows and translate them into scalable, reliable platform capabilities 
• Design, build, and maintain distributed services and APIs that manage compute workloads, data access, and execution lifecycles across HPC environments 
• Improve the usability, reliability, and performance of large-scale quantitative research workflows, with a strong focus on developer experience and operational excellence 
• Contribute to the design and implementation of scheduling, security, and observability solutions suitable for highly parallel, compute-heavy systems 
• Work with state-of-the-art infrastructure and modern software engineering practices to support massive compute pipelines and rapidly evolving research demands 
• Take ownership of complex technical problems, operating with a high degree of autonomy while making pragmatic trade-offs to deliver impact quickly and safely 

Your Present Skillset 

• BSc / MEng / PhD in Computer Science or a closely related field, or equivalent practical experience, with 5+ years of professional software development 
• Strong proficiency in at least one statically-typed programming language (e.g. C, C++, Java, Rust), with an interest in Go (prior experience welcome but not required). Experience with Python is a plus 
• Required: 5+ years of experience designing, implementing, and operating large-scale distributed systems in production environments 
• Preferred: Prior experience in quantitative finance or working closely with quantitative researchers and data-intensive workloads 
• Preferred Understanding of GPU programming models and performance considerations (e.g. CUDA, memory hierarchies, parallel execution), with exposure to ML frameworks such as PyTorch or TensorFlow 
• Solid understanding of systems concepts such as concurrency, performance, fault tolerance, and reliability 
• Comfortable working in fast-moving, rapidly evolving environments; proactive in learning new technologies and improving existing systems 
• Pragmatic engineering mindset, with a focus on delivering high-impact solutions while maintaining long-term platform health 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

ABOUT THE TEAM

Anduril's Air & Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.

This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like "why didn't track X and track Y associate?" We don't just track targets; we track our tracking systems and make them smarter.

WHAT YOU'LL DO

• Own tracking intelligence infrastructure end-to-end: Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production
• Automate tracking analysis: Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights
• Build autotuning capabilities: Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance
• Design human-in-the-loop tools: Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models
• Exploit tracking telemetry: Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training
• Deploy in constrained environments: Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts
• Manage the ML lifecycle: Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways
• Bridge domains: Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work
• Drive make/build decisions: Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems
• Work hands-on-keyboard: This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling

REQUIRED QUALIFICATIONS

• 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems
• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows
• Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)
• Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do
• Ability to work with streaming time-series data and engineer features from algorithm telemetry
• Experience building data catalogs, managing ground truth labels, and validating model performance
• Strong software engineering fundamentals—you can build maintainable, production-quality code independently
• Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)
• Ability to obtain and maintain a U.S. Top Secret SCI security clearance

PREFERRED QUALIFICATIONS

• Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints
• Background in defense, aerospace, or sensor systems
• Familiarity with containerization (Docker, Kubernetes) for model serving and deployment
• Experience with anomaly detection, root cause analysis, or automated diagnostics systems
• Knowledge of AutoML, hyperparameter tuning, or online learning techniques
• Understanding of radar systems, sensor fusion, or signal processing
• Experience building conversational or query interfaces for technical systems
• Familiarity with model registries and model-as-data artifact management
• Experience with distributed data processing (Spark, Dask) for large-scale telemetry analysis
• Formal coursework or training in MLOps, data science, or estimation theory
• Active U.S. Top Secret SCI clearance