The Data Platforms Health team builds native platform solutions that enable healthcare-specific platform capabilities. This development role in the Database Platforms department will contribute to AI-based features and workflows within the InterSystems Health Connect Cloud, InterSystems FHIR Server, InterSystems IRIS for Health and InterSystems Health Connect products.

The team needs a developer who can drive AI model and AI architecture design and development for intelligent tooling features for healthcare builders and administrators. The team also needs thought leadership to adopt evolving AI technologies so that InterSystems can be a leader in AI-driven features and workflows.

You are encouraged to apply if this role sounds interesting to you, even if you have doubts about being a competitive candidate. InterSystems prides itself on investing in its team members.

This position is in the InterSystems headquarters in Boston, MA.

Responsibilities

You will design, build, train, and deploy machine learning and agentic AI models, and consume LLMs and custom models using REST APIs. Working with product management and support specialists, you will design and implement data cleansing and feature extraction tools to improve the accuracy of predictions and data generation for a healthcare setting. Your responsibilities will include integrating the models into existing products and refining the models to meet evolving product needs.

All work must follow the team’s development workflow including use of source control, design and code reviews, and clear documentation. You are responsible for the quality of your code and must design automated tests to verify the effectiveness of the models and features.

This role will require you to respond to technical questions about expected product behavior and recommended best practices from support specialists and sales engineers. You will also collaborate with the release engineering team to ensure that your model and tools are appropriately deployed in cloud and on-prem environments.

The technology stack includes Python, AWS Bedrock, ObjectScript, and InterSystems IRIS.

As a leader, you are expected to create project plans with estimates, track execution against the plan, and proactively communicate anticipated delays in the project. With support from your manager, you will coordinate with other teams and overcome blockers and inter-team misalignments to deliver your projects. You will also provide thought leadership in the areas of Gen AI, agentic AI, and other evolving technologies, and encourage other team members to adopt these technologies for the platform.

Qualifications Required

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
• 8+ years of hands-on professional experience as a software developer or similar. Open to considering less experience in combination with a Graduate degree.
• Experience with data analysis, data cleansing, feature extraction, and model training.
• Experience with Python and common libraries like Pandas, NumPy, and Scikit-learn.
• Experience with model training and evaluation.
• Experience with Agent orchestration and runtime, MCP, tool registration, and multi-model routing.
• Experience with design or consumption of REST APIs.
• Strong verbal and written communication skills.

Preferred Qualifications

• Self-starter with curiosity to experiment and learn, and confidence to challenge the status quo.
• Professional experience with machine learning algorithms like neural networks and LLMs.
• Professional experience with programmatic access of LLMs and/or open-source models.
• Experience with observability of model performance and runtime costs.
• Experience with model governance and design of guardrails for model security.
• Professional experience with deep learning libraries like TensorFlow, PyTorch, or Keras.
• Professional experience with health messaging standards, especially FHIR.
• 3+ years of experience with other programming languages like Java, C#, C++, or ObjectScript.

We are an equal-opportunity employer and do not discriminate because of race, color, religion, sex, national origin, ancestry, marital status, veteran status, age, disability, sexual orientation or gender identity or expression or any other legally protected category. InterSystems is an E-Verify Employer in the United States.

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

Our Intern/Co-op program is designed to give you a real-world view of what it's like to be a Machine Learning Engineer at PathAI. You will be embedded directly into our AI product and ML Core teams, working on active projects that have a direct impact on our technical roadmap and business goals.

You will collaborate with a cross-functional team of scientists, engineers, and product managers to translate clinical needs into scalable ML solutions. This is your chance to contribute to meaningful work that directly relates to patient outcomes.

Timeline: This opportunity is for Fall 2026. We are looking for interns/co-ops who can commit to 3-6 months at PathAI (4-6 months preferred).

Location: We have offices in Boston, MA and New York, NY and would prefer interns/co-ops who can work from one of those locations—however, we will also consider remote candidates (US based only).

What You’ll Do

• Contribute to the design, development, and evaluation of machine learning models for our AI products, AI services and core ML models.
• Work on subprojects derived from our ongoing ML product roadmap, focusing on areas like model improvements with novel techniques.
• Partner with our MLOps and platform teams to help build and improve our ML pipelines.
• Participate in experimental design, analysis, and team knowledge-sharing initiatives like design reviews, technical forums and journal clubs.

What You Bring 

• Currently pursuing a M.S., or Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering with a focus on Computer Vision and Machine Learning.
• Strong programming skills in Python and hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow).
• A solid grasp of the fundamentals of machine learning and/or computer vision.
• Excellent communication skills and a desire to work in a highly collaborative, cross-functional environment.
• A passion for our mission to improve patient outcomes.
• [Nice-to-have] Any high impact contributions through any AI/ML publications, open-source projects and any AI based products.

We are looking for team players who are passionate about what they do and are willing to tackle any task to contribute to our goals. You could be exactly who we are looking for!

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 
PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.  

Mission Summary:

Motional’s onboard autonomous driving system team works at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s next-generation on-board autonomous driving system. As part of this team, the High Performance Compute Platform team is responsible for Motional’s current and next-generation onboard autonomous driving system. If you are a software engineer and love the idea of working on embedded AI hardware and software compute systems to create the next generation of autonomous vehicles, we would love to talk with you. We are hiring in a range of levels, from graduate engineers to Staff engineers.

What You'll Be Doing:

• Design and develop infrastructure software on various hardware platforms for applications such as vision processing, radar systems, safety monitoring, etc., to be run on self-driving vehicles.
• Design test harnesses for embedded software components and full systems
• Provide technical mentorship to engineers.
• Proactively work with cross-functional engineering teams to solve complex and interesting problems.
  
  

What We're Looking For:

• Experience with creating detailed requirements from use cases.
• Ability to lead a technical initiative, including breaking down work and guiding other engineers through execution.
• Experience writing software for embedded platforms in C and C++.
• Experience with Test-Driven Development (TDD).
• Experience working on embedded Linux / RTOSs.
• Experience working with networks (Ethernet, CAN etc.) and the common networking protocols.
• Experience with debugging on embedded platforms.
• Experience writing software in Python and experience doing automation with shell scripting.
• Experience working with ARM Cortex MCUs or Microprocessors.
  
  

Bonus Points (not required):

• Experience working with large data pipelines, and platforms that require deterministic execution.
• Experience using inter-system communication protocols such as I2C and SPI
• Experience deploying Machine Learning models.
• Experience working with GPUs.
• Experience working directly with the Linux kernel or Device Drivers.
• Experience with Simulation and Code Generation, and knowing when their use is appropriate.
• Experience working with Bazel.
• Experience integrating various sensors, including Cameras, IMUs, Radars, LIDARs.
• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

Motional’s onboard autonomous driving system team works at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s next-generation on-board autonomous driving system. As part of this team, the High Performance Compute Platform team is responsible for Motional’s current and next-generation onboard autonomous driving system. If you are a software engineer and love the idea of working on embedded AI hardware and software compute systems to create the next generation of autonomous vehicles, we would love to talk with you. We are hiring in a range of levels, from graduate engineers to Staff engineers.

What You'll Be Doing:

• Design and develop infrastructure software on various hardware platforms for applications such as vision processing, radar systems, safety monitoring, etc., to be run on self-driving vehicles.
• Design test harnesses for embedded software components and full systems
• Provide technical mentorship to engineers.
• Proactively work with cross-functional engineering teams to solve complex and interesting problems.
  
  

What We're Looking For:

• Experience with creating detailed requirements from use cases.
• Ability to lead a technical initiative, including breaking down work and guiding other engineers through execution.
• Experience writing software for embedded platforms in C and C++.
• Experience with Test-Driven Development (TDD).
• Experience working on embedded Linux / RTOSs.
• Experience working with networks (Ethernet, CAN etc.) and the common networking protocols.
• Experience with debugging on embedded platforms.
• Experience writing software in Python and experience doing automation with shell scripting.
• Experience working with ARM Cortex MCUs or Microprocessors.
  
  

Bonus Points (not required):

• Experience working with large data pipelines, and platforms that require deterministic execution.
• Experience using inter-system communication protocols such as I2C and SPI
• Experience deploying Machine Learning models.
• Experience working with GPUs.
• Experience working directly with the Linux kernel or Device Drivers.
• Experience with Simulation and Code Generation, and knowing when their use is appropriate.
• Experience working with Bazel.
• Experience integrating various sensors, including Cameras, IMUs, Radars, LIDARs.
• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

Motional’s onboard autonomous driving system team works at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s next-generation on-board autonomous driving system. As part of this team, the High Performance Compute Platform team is responsible for Motional’s current and next-generation onboard autonomous driving system. If you are a software engineer and love the idea of working on embedded AI hardware and software compute systems to create the next generation of autonomous vehicles, we would love to talk with you. We are hiring in a range of levels, from graduate engineers to Staff engineers.

What You'll Be Doing:

• Design and develop infrastructure software on various hardware platforms for applications such as vision processing, radar systems, safety monitoring, etc., to be run on self-driving vehicles.
• Design test harnesses for embedded software components and full systems
• Provide technical mentorship to engineers.
• Proactively work with cross-functional engineering teams to solve complex and interesting problems.
  
  

What We're Looking For:

• Experience with creating detailed requirements from use cases.
• Ability to lead a technical initiative, including breaking down work and guiding other engineers through execution.
• Experience writing software for embedded platforms in C and C++.
• Experience with Test-Driven Development (TDD).
• Experience working on embedded Linux / RTOSs.
• Experience working with networks (Ethernet, CAN etc.) and the common networking protocols.
• Experience with debugging on embedded platforms.
• Experience writing software in Python and experience doing automation with shell scripting.
• Experience working with ARM Cortex MCUs or Microprocessors.
  
  

Bonus Points (not required):

• Experience working with large data pipelines, and platforms that require deterministic execution.
• Experience using inter-system communication protocols such as I2C and SPI
• Experience deploying Machine Learning models.
• Experience working with GPUs.
• Experience working directly with the Linux kernel or Device Drivers.
• Experience with Simulation and Code Generation, and knowing when their use is appropriate.
• Experience working with Bazel.
• Experience integrating various sensors, including Cameras, IMUs, Radars, LIDARs.
• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

We want people who are passionate about solving complex cloud infrastructure challenges for our customers.

We are looking for a Solutions Architect with expertise in cloud infrastructure to support the success and retention of DigitalOcean's high-value customers. Reporting to the Manager of Solutions Architecture, you will work directly with Customer Success, Sales, and Support functions to retain and grow DigitalOcean’s largest and fastest growing customers. You will be the technical subject matter expert on DigitalOcean’s portfolio, advising on best practices and guiding customers to the optimal solution to meet their business objectives. You will work closely with other functions within DigitalOcean such as Product, Engineering, and Operations to ensure the needs and insights from the local markets are funneled appropriately back into DigitalOcean's products and services.

This is an amazing growth opportunity for a highly motivated individual to establish and expand our presence within the region. Technical depth, excellent communication skills, and a self-starter mentality are needed.

 What You Will Be Doing: 

As a Senior Solutions Architect II, you will serve as a technical partner to the success and sales teams. You will perform architecture reviews, proof of concepts, demos, uncover customer technical needs, and demonstrate DigitalOcean's value proposition. Working with AEs, GAM and TAM counterparts, you'll help retain large customers and expand their workloads on DigitalOcean.

• Develop deep expertise in the DigitalOcean product portfolio, the evolving cloud landscape, and AI/ML platforms.
• Advocate for customers by providing feedback to product teams, overcoming adoption blockers, and driving new feature development.
• Offer personalized onboarding assistance and guide customers through their cloud journey, resolving technical blockers.
• Design and review architecture tailored to specific business use cases, ensuring optimal solutions for customers.
• Implement and oversee infrastructure management best practices to secure and optimize cloud resources.
• Identify opportunities for cost reduction and performance improvement, helping customers make data-driven cloud optimization decisions.
• Conduct technical consultation sessions, workshops, and training for customers to empower them in managing cloud infrastructure.
• Partner with Growth Account Managers and Customer Success Managers to create success plans that ensure customer retention, expansion, and satisfaction.
• Provide regular presentations articulating product benefits, functionality, and technical solutions.
• Demonstrate strong expertise in AI/ML frameworks like TensorFlow and PyTorch and usage of platforms like Hugging Face, with experience deploying and fine-tuning LLMs and GenAI models.
• Optimize GPU workloads using CUDA or TensorRT and build scalable AI applications like chatbots, inference services or recommendation systems using Kubernetes and NFS.
• Design AI solutions tailored to business needs, manage data pipelines, and integrate databases.
• Liaise with Engineering and Support teams to resolve escalations and growth obstacles in a timely fashion.
• Identify and drive process improvement opportunities and promote technical best practices across the organization.
• Contribute to internal and external documentation, such as "The Navigator’s Guide to DO."
• Build tools to enhance the experience of Premium Support customers and improve platform usability.
• Demonstrate proficiency in DevOps tools like Docker, Terraform, and CI/CD pipelines.
• Leverage expertise in Kubernetes, Managed DBaaS, and the DigitalOcean GenAI Platform.
• Work both independently and collaboratively with a global team of Solutions Architects, Customer Success Managers, and Account Managers.

What We'll Expect From You:

• Proven professional experience with cloud infrastructure, AI/ML platforms, or equivalent education.
• Expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch) and familiarity with platforms like Hugging Face.
• Experience deploying and fine-tuning LLMs (e.g., DeepSeek, Llama, Claude, GPT-4) and GenAI models.
• Deep knowledge of Linux, distributed systems, Kubernetes, NFS, Object Storage, and GPU optimization techniques (e.g., CUDA, TensorRT).
• Hands-on experience leveraging vllm and various quantization methods (e.g., INT4, INT8, and FP8) for efficient model deployment.
• Programming/development experience, particularly in building AI-powered applications (e.g., recommendation systems, chatbots).
• Experience working in a post-sales or technical consultant role with a focus on AI/ML and cloud solutions.
• Knowledge of provisioning, deployment strategies, and tools like Terraform, Kubernetes, and Docker.
• Passionate about customer experience and leveraging AI/ML to solve complex business problems.
• Track record of developing successful technical solutions, including AI/ML use cases, to address customer challenges.
• Ability to balance the demands of multiple stakeholders, define priorities, and set appropriate expectations.
• Strong understanding of AI/ML data pipelines, cloud-native databases, and integration strategies.
• Passionate about technology, open source projects, and applying cutting-edge AI/ML innovations.
• Strong communication skills, with the ability to explain technical AI/ML concepts in clear and concise terms.
• Quickly learn DigitalOcean systems and adapt to rapid changes in cloud and AI/ML trends.
• Fluency in English.
• Highly motivated with a self-starter mentality, ready to tackle AI/ML challenges head-on.

Extra Credit: 

• Cloud certifications are highly desirable (AWS/GCP/Oracle/Azure)
• NVIDIA Certifications around GPU and AI/ML
• Networking: Cisco/Juniper Certifications
• Programming/Scripting: Ruby, Python, Go, Bash
• Source Code: Git
• Automation: Terraform, Ansible, Chef, Puppet, Saltstack
• Virtualization: KVM, Xen
• Databases: MongoDB, MySQL, Redis, PostgreSQL
• Open Source: CoreOS, Docker, Kubernetes (CKA/CKE), Vagrant
• DigitalOcean: API, libraries, services
• Linux: RHCSA/RHCE

Compensation Range: 

• Base: $150,000 - $182,000 / Target OTE: $200,000 - 240,000

*This is a remote role

JR: 2026-7695

#LI-Remote

We are looking for a Manager, Customer Success Engineering (AI & Cloud Support) who is passionate about delivering exceptional support experiences and building high-performing teams.

As a Manager, CSE (AI & Cloud Support) at DigitalOcean, you will operate as a Support Manager leading a team of Customer Success Engineers (CSEs) supporting strategic customers across cloud infrastructure and emerging AI/ML workloads. Reporting to the Senior Manager of Customer Success Engineering, you will own day-to-day support operations, team performance, and customer experience outcomes.

This role is focused on driving operational excellence while building strong technical depth within the team across AI, GPUs, Kubernetes, Databases and core cloud offerings. You will play a key role in ensuring consistent support coverage, developing SMEs, and partnering cross-functionally with Product and Engineering to improve both customer experience and product quality.

The ideal candidate brings a deep understanding of cloud and AI/ML ecosystems, including machine learning operations (MLOps), and enterprise support, combined with a passion for innovation and automation, and a proven ability to lead teams in delivering “white-glove” experiences for strategic customers.

What You’ll Do:

Team Leadership & Development

• Lead, hire, train, mentor and develop a high-performing team of Customer Success Engineers (CSEs), driving accountability, performance, and career growth
• Establish performance metrics (KPIs/SLAs) and conduct regular 1:1s, performance reviews, and career development planning.
• Own end-to-end support operations, including queue management, escalations, and shift planning to ensure consistent 24x7 coverage.
• Drive improvements in key support metrics such as CSAT, response times, resolution times, and overall support quality.
• Build and strengthen technical expertise within the team across core areas such as Kubernetes (DOKS), Databases, Compute, and AI/ML workloads.
  
  

Strategic Customer Support

• Act as the ultimate point of technical escalation for our largest, most strategic enterprise customers across Cloud and AI/ML workloads, stepping in to manage critical incidents and high-severity (Sev1/Sev2) issues.
• Design and implement customized support plans, SLAs, and escalation pathways tailored to the needs of strategic accounts.
• Partner closely with Technical Account Managers (TAMs), Growth Account Managers (GAM) to conduct Executive Business Reviews (EBRs) and ensure customers are maximizing the value of our Cloud and AI/ML products.
• Proactively identify risks and opportunities within strategic accounts to improve customer experience, adoption, and retention.

Technical & Cross-Functional Operations

• Serve as the Voice of the Customer (VoC) to Product and Engineering teams, synthesizing support data to advocate for bug fixes, feature requests, and UX improvements.
• Own and continuously improve escalation protocols between AI/ML Support and CloudOps, Infrastructure Engineering, and Product — including Jira escalation routing, Sev1 bridge management, and post-incident documentation.
•  Own the development and maintenance of SOPs, escalation runbooks, HVC support playbooks, and knowledge base content — treating documentation infrastructure as a core operational lever for team scalability.
• Contribute to the vision for AI and automation within support—building intelligent tooling and driving the team toward an automation-first model to improve efficiency, scalability, and customer experience.
• Foster a culture of continuous learning, ensuring the team stays ahead of evolving cloud technologies, AI/ML frameworks, and industry trends.

Key Metrics:

• Customer Satisfaction (CSAT) for strategic accounts
• Time to Response and Resolution (TTR) for strategic customers
• Tier 1 resolution rate vs. escalation rate
• Time-to-escalation and engineering handoff SLA adherence
• SLA adherence and escalation response times
• Support productivity and quality (QA scores)
• Post-incident documentation completion rate

What You’ll Add to DigitalOcean:

• Experience: 5+ years of experience in Technical Support, Customer Success, or Technical Account Management within B2B SaaS, Cloud, or AI/ML environments ideally including experience supporting AI-native, high-growth companies with 24x7 production dependencies on GPU infrastructure.
• Leadership: 2+ years of people management experience leading technical, customer-facing teams, preferably in a high-growth, post-acquisition, or rapidly scaling environment.
• Technical Domain Expertise: Solid understanding of AI/ML concepts, including Generative AI, Large Language Models (LLMs), natural language processing (NLP), and MLOps. Deep familiarity with GPU infrastructure (NVIDIA H100/H200, bare metal GPU provisioning) and AI inference workloads is strongly preferred.
• Coding/Integration: Proficiency in reading and debugging code (Python preferred) and troubleshooting RESTful APIs and cloud architecture.
• Communication: Excellent verbal and written communication skills, with the ability to translate complex technical or AI concepts for diverse audiences OR to both highly technical engineers and non-technical business executives.
• Problem-Solving: Proven ability to remain calm under pressure and de-escalate high-stakes situations with enterprise clients.

Preferred Qualifications:

• Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and AI toolchains (e.g., LangChain, Hugging Face).
• Experience with major cloud platforms (AWS, Google Cloud, Azure) and their native AI/ML services.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• ITIL or equivalent service management certification

Compensation Range: 

• $125,000 - $153,000

*This is a remote role

JR: 2026-7692

#LI-Remote

Position

Manifold's AI team is actively training protein foundation models on our proprietary experimental datasets. Our generative antibody design model, mBER, has already demonstrated controllable de novo binder design across multiple million-scale screening campaigns, and the team is now scaling foundation model capabilities to push well beyond current performance. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifold's data, bringing additional depth in pre-training methodology, architecture development, and large-scale training. Your work will directly improve mBER's design capabilities and unlock new modeling paradigms for the broader team. You'll own foundation model projects end-to-end, from architecture selection and training infrastructure to evaluation against real experimental outcomes, while contributing to the team's shared research agenda.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Advance the team's ongoing foundation model training efforts—pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data
• Bring depth in training methodology, architecture selection, and optimization to complement the existing team's expertise
• Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs
• Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities
• Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis
• Establish best practices for mixed-precision training, gradient checkpointing, and computational efficiency at scale
• Produce clear documentation and analysis supporting architecture and training decisions

Required Qualifications

• Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results
• Strong familiarity with AlphaFold architecture and training methodology
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning
• Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing
• Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques
• Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets
• Strong statistical analysis and experimental design skills
• Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Self-directed researcher who can balance guidance with independence
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)
• Experience with protein language models (e.g., ESM family)
• Published research in computational biology, protein design, or structural biology
• Experience training on proprietary or domain-specific biological datasets
• Familiarity with Ray for distributed computing
• Experience with Kubernetes (EKS) and cloud computing platforms (AWS)
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments
• Previous biotech/pharma industry experience

This Role Might Be Perfect For You If:

• You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field
• You're excited about pushing beyond public model performance by leveraging unique, large-scale in vivo screening data
• You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-knit, world-class team
• You want your models to directly impact real drug discovery programs

If you're excited to train the next generation of protein foundation models on uniquely powerful experimental data, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

Position Summary

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities.

Primary Responsibilities

• Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
• Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
• Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
• Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
• Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
• Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
• Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
• Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.

Qualifications

• Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 6+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
• Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with CI/CD pipelines, testing automation, and version control using Git.
• Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.
• Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
• Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

This is an exempt position. The annualized base pay range for this role is expected to be between $140,000-$180,000.  Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set.  The base pay is just one component of the total compensation package for employees.  Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

Position

Manifold Bio is seeking an exceptional Machine Learning Scientist to lead research initiatives within our AI team and drive innovation in protein design methodologies. This role will support and enrich the team’s current generative de novo protein design capabilities, as well as break ground on building foundational models on relevant protein therapeutic properties including binding affinity, developability, and in vivo biodistribution and pharmacokinetic (PK) properties. You will be expected to independently conceive, design and execute high-impact research projects that push the boundaries of AI applications in protein design, while contributing to publications and representing Manifold’s scientific contributions to the broader research community.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Lead independent research projects exploring novel AI/ML approaches for protein design and optimization
• Investigate and integrate novel data types to enhance our binder design capabilities and therapeutic development
• Develop and validate cutting-edge machine learning methodologies for molecular engineering applications
• Mentor and guide junior scientists and engineers in advanced ML techniques and research methodologies
• Drive strategic research planning and identify promising new directions for AI-driven drug discovery
• Lead cross-functional teams to translate research breakthroughs into therapeutic applications
• Provide scientific expertise for business development opportunities and strategic partnerships

Required Qualifications

• PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, or related field
• 5+ years of industry experience in machine learning research, preferably in biotechnology or pharmaceutical settings
• Expert-level proficiency in PyTorch and/or JAX with extensive experience in novel architecture development
• Deep expertise in protein structure analysis, molecular dynamics, computational biology, or structural bioinformatics
• Extensive knowledge of modern AI advances in protein analysis, design, and optimization
• Advanced understanding of deep learning architectures, particularly transformers for molecular applications, as well as generative modeling approaches (autoregressive, diffusion, flow-matching, etc.)
• Proven experience leading research teams and mentoring junior scientists
• Strong background in statistical analysis, experimental design, and rigorous scientific methodology
• Experience with distributed computing, high-performance computing, and large-scale ML system design
• Outstanding written and verbal communication skills with experience presenting to scientific and business audiences
• Demonstrated ability to translate research innovations into practical applications

Preferred Qualifications

• Previous experience in biotech/pharma industry with focus on AI-driven drug discovery
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Experience with large-scale data engineering, cloud platforms, and production ML systems
• Demonstrated track record of published research in top-tier ML, computational biology, or protein design journals
• Track record of successful grant funding and research proposal writing
• Experience with regulatory considerations for AI applications in therapeutic development
• Strong network within the computational biology and protein design research communities
• Demonstrated leadership in cross-functional scientific teams
• Experience with intellectual property development and patent applications

This Role Might Be Perfect For You If

You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas

• You have rich AI/ML experience and are looking to pivot into biotech

If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

Position

Manifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production-ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting-edge ideas into robust, scalable systems.

This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. 

Responsibilities

• Implement and optimize machine learning models for protein design
• Build and maintain scalable data processing pipelines for large-scale protein and molecular datasets
• Develop and deploy ML infrastructure for distributed training and inference across GPU clusters
• Collaborate with research scientists to translate experimental ML approaches into production-ready code
• Design and execute ML experiments with clear hypotheses and rigorous analysis
• Optimize model performance and computational efficiency for large-scale protein design tasks
• Build tools and utilities to support rapid prototyping and experimentation by the research team

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field
• 2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications
• Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)
• Experience with distributed computing and GPU optimization techniques
• Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences
• Understanding of modern deep learning architectures and optimization techniques
• Experience implementing research papers or translating ML approaches to production systems
• Proficiency with version control (Git), testing frameworks, and software engineering best practices
• Strong problem-solving skills and ability to work independently on technical challenges
• Excellent written and verbal communication skills for cross-functional collaboration

Preferred Qualifications

• Experience training LLMs or diffusion generative models
• Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)
• Background in computational biology, bioinformatics, or structural biology
• Experience with large-scale data engineering and ETL pipelines
• Familiarity with MLOps practices and model deployment frameworks

This Role Might Be Perfect For You If

You are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas

• You enjoy translating research ideas into high impact, productionized, scalable code
• You have rich AI/ML experience and are looking to pivot into biotech

If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to careers@manifold.bio.

Base Salary Range: $140,000-225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

This role is eligible for:

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on-site gym, onsite lunch, and commuter support

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from Motional’s driving data, working at the intersection of large-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self-supervised learning approaches to improve data search and retrieval.
• Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments.
• Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios.
• Monitor Production Performance: Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services.
• Learn and Apply Best Practices: Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to technical documentation.
• Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions.
  
  

What We're Looking For (Must-Haves):

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics.
• Strong proficiency in Python with the ability to write clean, modular, and well-documented code.
• Working knowledge of version control, unit testing, and basic software design patterns.
• Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy.
• A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics.
• A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment.
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

You’re an engineer who is excited to work on cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. You have a solid foundation in areas like detection, classification, segmentation, or tracking—whether through previous roles, internships, or research—and you’re eager to apply those skills in an impactful, production-focused environment.

You’re curious, motivated, and ready to learn from experienced teammates. You enjoy solving challenging technical problems, asking thoughtful questions, and collaborating with others to build high-quality software.

You’re excited about contributing to the perception stack for self-driving vehicles and learning what it means to build technology for safety-critical, open-world applications.

What you’ll be doing:

• Contribute to the design, development, and improvement of perception components that support downstream modules within the autonomy system.
• Help analyze perception system performance to identify areas for improvement and participate in efforts to address them.
• Work with teammates to explore creative approaches to “long-tail” challenges—for example, experimenting with synthetic data, data mining techniques, or new model architectures.
• Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
• Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We’re Looking for:

• Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
• Solid understanding of fundamental deep learning concepts and computer vision techniques
• Experience building, training, or evaluating neural networks
• Proficiency in Python and familiarity with libraries like PyTorch or similar
• Exposure to software engineering fundamentals—version control, code reviews, testing, and collaborative development
• Clear communication skills and willingness to ask questions, share ideas, and work with others

Bonus Points:

• Previous experience with sensor fusion and/or previous exposure to the AV industry
• Experience shipping a “real” product and balancing ongoing support with next-generation innovation
• Exposure to safety-critical products or components
• Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with TensorRT and model quantization

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discover: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

As a seasoned expert in Perception Capabilities, you'll field cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. Your breadth of experience spans critical problem areas like detection, classification, segmentation, and tracking, along with essential supporting functions such as data engines, labeling policies, training pipelines, model optimization, simulation, and testing. You're driven by seeing your work significantly impact the product, taking initiative and owning outcomes.

You're eager to apply these skills to the perception stack of self-driving vehicles. You understand the unique challenges of safety-critical, open-world applications and are committed to ensuring engineering excellence and rigorous evaluation.

You'll devise creative solutions to complex problems, collaborating with interdisciplinary, capability-focused teams across the organization to design, implement, and validate end-to-end solutions.

What you’ll be doing:

• Lead the development and implementation of solutions that span across perception components and directly interface with downstream modules in the autonomy system.
• Analyze the performance of the perception subsystem to proactively identify potential limitations with respect to a set of system-level capabilities. Document potential hazards and lead the cross-team efforts to eliminate or mitigate them.
• Collaborate on innovative solutions to address “long tail” challenges. For example, developing techniques for synthetic data generation, advanced data mining, or leveraging language embeddings in our onboard models.
• Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
• Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We’re Looking for:

• Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
• Strong leadership skills in executing large, complex technical initiatives
• Track record of successful interdisciplinary work and product-focused engineering
• Broad understanding of Deep Learning algorithms, architectures, and applications
• Experience designing, training, and analyzing neural networks for applications relevant to perception
• Fluency in Python, including common libraries for deep learning (PyTorch), scientific computing, and data analysis and visualization
• Advanced knowledge of software engineering principles, including software design, source control management, build processes, code reviews, and  testing methods 
• Excellent communication and interpersonal skills
• Experience mentoring and leading others

Bonus Points:

• Experience shipping a “real” product and balancing ongoing support with next-generation innovation
• Experience developing safety-critical products or components
• Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with TensorRT and model quantization

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems, and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Motional’s global headquarters are located at 100 Northern Avenue in Boston, MA. Nestled in the bustling Seaport district with sweeping views of Boston Harbor and downtown Boston, the office is located close to major transit lines and a quick walk to various restaurants and popular attractions.

Mission Summary:

Motional’s CORE team is responsible for our vehicle’s Compute and Onboard Runtime Environment. We are creating a world leading AI compute platform for autonomous vehicles. This is the system that executes the software and neural networks that make our vehicles autonomous. 

The Next-Gen Technologies team is part of CORE. We work at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s on-board vehicle architecture. If you are a software engineer and love the idea of working on embedded AI hardware and software systems to create the next generation of autonomous vehicles, we would love to talk with you. 

What You’ll Be Doing:

As a senior engineer in the Next-Gen Technologies team, you will help us improve the compute performance of our current generation and next-generation autonomous driving systems through participating in full lifecycle development from idea to proofs-of-concept to production. 

Specifically, you will:

• Focus deeply on ML model deployment, integration of multiple ML models, and ML model optimization on embedded compute platforms.
• Dive deep into the full ML software stack. Analyze ML workload performance on a variety of hardware processors, optimize ML models, improve ML software, and help us continually improve our stack through the application of efficient and effective ML approaches. 
• Design, develop, test, integrate, and optimize software and tools on a variety of ML compute architectures.
• Collaborate with deep learning experts in perception, prediction, and other autonomous driving application areas to enable algorithms on GPU, NPU, and other ML accelerator architectures.
• Optimize the utilization of GPU/NPU resources and sharing of GPU/NPU access across multiple programs running on the same system.
• Lead designs to determine the needs of the system and how to best meet those needs through continually improving our ML software stack.
• Advise peers and management on technical matters.

What We’re Looking For:

• Experience with machine learning accelerators, including GPUs, NPUs, TPUs, and their programming environments, including CUDA, TensorRT, or similar technologies.
• Strong experience with modern C++ development in a Linux environment.
• Experience with parallel and high-performance computing.
• Comfortable with experimentation and evaluating different options as we work towards finding solutions that work. 
• A degree in Software Engineering, Computer Science, Electrical or Electronic Engineering, or similar technical field of study, or you have equivalent knowledge gained through your practical experience.

Preferred, but not required:

• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
• Experience with embedded systems development for ARM-based system-on-chip architectures.
• Experience working in a MLOps or DevOps environment.
• Passion for self-driving technology and its potential for positive impact on the world.
  
  

This role is hybrid from our Boston office. It requires two in-office days each week, ideally Tuesday and Thursday.

On our Behaviors team (Prediction and ML Planner), you will have the opportunity to work with world-class ML engineers, whose mission is to make self-driving vehicles a reality and to create a positive social impact. The Behaviors team develops ML models that learn how different agents navigate complex real-world traffic scenarios. We leverage these models to set the behavior of our self-driving vehicle, and predict the behavior of other agents such as vehicles and vulnerable road users. 

We are looking for proven leaders and technical experts who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.

What You’ll Be Doing:

• Define and execute motion planning and prediction projects that improve our self-driving vehicles’ capability to safely, comfortably and legally navigate complex traffic scenarios
• Lead, manage and grow a team of engineers
• Design and lead the implementation of behavior models that leverage the latest advancements in machine learning, generative AI and reinforcement learning
• Productionize and deploy solutions onto autonomous vehicle fleets
• Collaborate with perception, simulation, data platform and integration teams to validate and enhance your products’ on-road performance
• Communicate strategies, progress and challenges to executive leadership

What We’re Looking for:

• Masters or Ph.D. in Computer Science or a related technical field; or equivalent industry experience
• Proven leadership skills at executing large, complex technical initiatives
• Extensive experience managing and leading engineers
• Experience with deep learning frameworks such as TensorFlow or PyTorch
• Fluency in Python, including standard scientific computing libraries
• Proven track record of designing, developing and deploying ML solutions for autonomous vehicles or robotics
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods 
• Excellent communication and interpersonal skills

Bonus Points:

• Experience with embedded systems and real-time optimization, especially in the autonomous driving industry
• Experience with state of the art generative AI, and/or reinforcement learning, paradigms
• Proven track record of publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• Strong programming skills in C++ and/or CUDA programming

On our Behaviors team (Prediction and ML Planner), you will have the opportunity to work with world-class ML engineers, whose mission is to make self-driving vehicles a reality and to create a positive social impact. The Behaviors team develops ML models that learn how different agents navigate complex real-world traffic scenarios. We leverage these models to set the behavior of our self-driving vehicle, and predict the behavior of other agents such as vehicles and vulnerable road users. 

We are looking for proven leaders and technical experts who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.

What You’ll Be Doing:

• Define and execute motion planning and prediction projects that improve our self-driving vehicles’ capability to safely, comfortably and legally navigate complex traffic scenarios
• Lead, manage and grow a team of engineers
• Design and lead the implementation of behavior models that leverage the latest advancements in machine learning, generative AI and reinforcement learning
• Productionize and deploy solutions onto autonomous vehicle fleets
• Collaborate with perception, simulation, data platform and integration teams to validate and enhance your products’ on-road performance
• Communicate strategies, progress and challenges to executive leadership

What We’re Looking for:

• Masters or Ph.D. in Computer Science or a related technical field; or equivalent industry experience
• Proven leadership skills at executing large, complex technical initiatives
• Extensive experience managing and leading engineers
• Experience with deep learning frameworks such as TensorFlow or PyTorch
• Fluency in Python, including standard scientific computing libraries
• Proven track record of designing, developing and deploying ML solutions for autonomous vehicles or robotics
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods 
• Excellent communication and interpersonal skills

Bonus Points:

• Experience with embedded systems and real-time optimization, especially in the autonomous driving industry
• Experience with state of the art generative AI, and/or reinforcement learning, paradigms
• Proven track record of publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• Strong programming skills in C++ and/or CUDA programming

On Behaviors, you'll have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality and create positive social impact. Our team works on the tech stack responsible for understanding highly complex driving scenes and generating safe, comfortable driving trajectories for our robotaxi using the latest deep neural networks. We are looking for engineers who are passionate about Level 5 autonomous driving, excited by intellectual challenges, and interested in pursuing career growth with a fast growing company.

What You’ll Be Doing:

You will be directly responsible for training, evaluation, and deployment of our ML-based scene understanding, behavior prediction, and planning models. Day to day responsibilities include:

• Designing and executing high value experiments based on collaborative input from other machine learning engineers
• Prototyping and implementing continuously improving metrics to evaluate the performance of our behavioral models across many desired scenarios and robotaxi capabilities
• Timely release of major model updates, including analysis of both off-line and on-road evaluation data
• Staying up to date with the latest trends in our fast moving industry, and proposing new architectures and network designs based on published literature
• Maintaining a high quality training and evaluation code-base, including portions of our training stack dedicated to dataset generation and evaluation

What We’re Looking For:

• In-depth understanding of common Machine Learning and Deep Learning algorithms
• Experience designing, training, and analyzing neural networks for at least one of the following applications: motion planning, object detection, sensor fusion, motion prediction, and/or multi-object tracking
• Strong track record of tackling less well-defined problems, and executing efficiently to deliver the successful outcome
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, and  testing methods
• Experience with PyTorch or other python-based deep learning frameworks
• Experience working with large data sets and deriving insights from data
• Experience mentoring and collaborating with others

Education
Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience

Bonus Points

• Publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• PhD in machine learning, computer vision, or robotics vision
• Experience working on autonomous vehicles or related fields
• Experience in deploying models into real-world environments
• Strong programming skills in C++ and/or CUDA programming
• This opportunity can support remote work within the United States, with occasional travel.

Working Conditions

While performing the duties of this job, the employee is frequently required to convey and detect communication

The employee occasionally required to remain stationary at times for up to 50% of working time, move in designated working areas; & operate and use office equipment

The employee may occasionally move up to 50 pounds

On Behaviors, you'll have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality and create positive social impact. Our team works on the tech stack responsible for understanding highly complex driving scenes and generating safe, comfortable driving trajectories for our robotaxi using the latest deep neural networks. We are looking for engineers who are passionate about Level 5 autonomous driving, excited by intellectual challenges, and interested in pursuing career growth with a fast growing company.

What You’ll Be Doing:

You will be directly responsible for training, evaluation, and deployment of our ML-based scene understanding, behavior prediction, and planning models. Day to day responsibilities include:

• Designing and executing high value experiments based on collaborative input from other machine learning engineers
• Prototyping and implementing continuously improving metrics to evaluate the performance of our behavioral models across many desired scenarios and robotaxi capabilities
• Timely release of major model updates, including analysis of both off-line and on-road evaluation data
• Staying up to date with the latest trends in our fast moving industry, and proposing new architectures and network designs based on published literature
• Maintaining a high quality training and evaluation code-base, including portions of our training stack dedicated to dataset generation and evaluation

What We’re Looking For:

• In-depth understanding of common Machine Learning and Deep Learning algorithms
• Experience designing, training, and analyzing neural networks for at least one of the following applications: motion planning, object detection, sensor fusion, motion prediction, and/or multi-object tracking
• Strong track record of tackling less well-defined problems, and executing efficiently to deliver the successful outcome
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, and  testing methods
• Experience with PyTorch or other python-based deep learning frameworks
• Experience working with large data sets and deriving insights from data
• Experience mentoring and collaborating with others

Education
Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience

Bonus Points

• Publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• PhD in machine learning, computer vision, or robotics vision
• Experience working on autonomous vehicles or related fields
• Experience in deploying models into real-world environments
• Strong programming skills in C++ and/or CUDA programming
• This opportunity can support remote work within the United States, with occasional travel.

Working Conditions

While performing the duties of this job, the employee is frequently required to convey and detect communication

The employee occasionally required to remain stationary at times for up to 50% of working time, move in designated working areas; & operate and use office equipment

The employee may occasionally move up to 50 pounds

About the Position

As a Senior Data Scientist - Medical Imaging at Formation Bio, you will lead our efforts to integrate advanced imaging analytics and AI into our drug development pipeline. In this role, you'll pioneer the application of computer vision and deep learning to medical imaging data (MRI, X-ray, CT, PET) to accelerate clinical trials and improve patient outcomes. You'll develop state-of-the-art models for automated image analysis, disease progression monitoring, and treatment response assessment. Working closely with clinical teams, radiologists, and drug development experts, you'll translate complex imaging data into actionable insights that drive key decisions across our portfolio. This position offers a unique opportunity to revolutionize how medical imaging is leveraged in pharmaceutical development while directly impacting patient care.

Responsibilities

• Lead the development of advanced deep learning models for medical image analysis, focusing on MRI and X-ray modalities for clinical trial applications
• Design and implement automated image processing pipelines for disease detection, segmentation, and quantification across multiple therapeutic areas
• Create innovative approaches for longitudinal image analysis to track disease progression and treatment efficacy in clinical trials, incl. AI-based biomarkers and surrogate endpoints
• Collaborate with clinical operations to integrate imaging AI solutions into trial protocols and workflows
• Partner with regulatory teams to ensure imaging biomarkers and AI models meet FDA standards for clinical trial endpoints
• Build scalable infrastructure for processing and analyzing large-scale medical imaging datasets from multi-site clinical trials
• Present imaging insights and model performance to executive leadership and external partners

About You

Required Qualifications 

• PhD in Computer Science, Biomedical Engineering, Medical Physics, or related field with focus on medical imaging
• 5+ years of post-PhD industry experience developing production-grade AI models for medical imaging applications
• Deep expertise in computer vision and deep learning architectures (CNNs, U-Net, Vision Transformers) for medical image analysis
• Proven track record with MRI and X-ray image analysis, including hands-on experience with DICOM standards and medical imaging software
• Strong programming skills in Python with expertise in PyTorch/TensorFlow and medical imaging libraries
• Demonstrated ability to collaborate with radiologists and clinical teams to translate medical insights into technical solutions
• Strong communication skills with proven ability to present complex imaging analytics to diverse stakeholders

Preferred Qualifications 

• Experience with regulatory submissions involving imaging endpoints or AI-based image analysis tools
• Experience with multi-modal imaging fusion and analysis (combining MRI, CT, PET data)
• Knowledge of clinical trial design and experience with imaging core lab operations
• Experience with federated learning or privacy-preserving techniques for medical imaging
• Understanding of radiomics and quantitative imaging biomarker development
• Experience with 3D image reconstruction and volumetric analysis

Total Compensation Range: $177,500 - $232,000

About the Position

As a Principal Data Scientist - Medical Imaging at Formation Bio, you will lead our efforts to integrate advanced imaging analytics and AI into our drug development pipeline. In this role, you'll pioneer the application of computer vision and deep learning to medical imaging data (MRI, X-ray, CT, PET) to accelerate clinical trials and improve patient outcomes. You'll develop state-of-the-art models for automated image analysis, disease progression monitoring, and treatment response assessment. Working closely with clinical teams, radiologists, and drug development experts, you'll translate complex imaging data into actionable insights that drive key decisions across our portfolio. This position offers a unique opportunity to revolutionize how medical imaging is leveraged in pharmaceutical development while directly impacting patient care.

Responsibilities

• Lead the development of advanced deep learning models for medical image analysis, focusing on MRI and X-ray modalities for clinical trial applications
• Design and implement automated image processing pipelines for disease detection, segmentation, and quantification across multiple therapeutic areas
• Create innovative approaches for longitudinal image analysis to track disease progression and treatment efficacy in clinical trials, incl. AI-based biomarkers and surrogate endpoints
• Collaborate with clinical operations to integrate imaging AI solutions into trial protocols and workflows
• Partner with regulatory teams to ensure imaging biomarkers and AI models meet FDA standards for clinical trial endpoints
• Build scalable infrastructure for processing and analyzing large-scale medical imaging datasets from multi-site clinical trials
• Present imaging insights and model performance to executive leadership and external partners

About You

Required Qualifications 

• PhD in Computer Science, Biomedical Engineering, Medical Physics, or related field with focus on medical imaging
• 5+ years of post-PhD industry experience developing production-grade AI models for medical imaging applications
• Deep expertise in computer vision and deep learning architectures (CNNs, U-Net, Vision Transformers) for medical image analysis
• Proven track record with MRI and X-ray image analysis, including hands-on experience with DICOM standards and medical imaging software
• Strong programming skills in Python with expertise in PyTorch/TensorFlow and medical imaging libraries
• Demonstrated ability to collaborate with radiologists and clinical teams to translate medical insights into technical solutions
• Strong communication skills with proven ability to present complex imaging analytics to diverse stakeholders

Preferred Qualifications 

• Experience with regulatory submissions involving imaging endpoints or AI-based image analysis tools
• Experience with multi-modal imaging fusion and analysis (combining MRI, CT, PET data)
• Knowledge of clinical trial design and experience with imaging core lab operations
• Experience with federated learning or privacy-preserving techniques for medical imaging
• Understanding of radiomics and quantitative imaging biomarker development
• Experience with 3D image reconstruction and volumetric analysis

Total Compensation Range: $204,500 - $267,000

We are seeking an innovative and experienced Embodied AI Researcher, focusing on the development and application of foundation models in robotics. The successful candidate will contribute to cutting-edge research, designing models and experiments that advance the field of robotics through deep learning and zero-shot generalization.

Responsibilities:

• Train foundation models for robotics, utilizing architectures similar to RT-X.
• Align text-based reasoning processes with low-level robotic control policies.
• Design and conduct experiments to evaluate zero-shot generalization in robotic systems.
• Collaborate with interdisciplinary teams to integrate AI models into practical robotic applications.
• Document and present research findings at internal and external conferences.

Qualifications:

• PhD or equivalent research experience in Deep Learning and Robotics.
• Proven track record of publications in top-tier conferences such as CoRL, ICRA, and NeurIPS.
• Practical experience with imitation learning and behavior cloning techniques.
• Strong programming skills in Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
• Excellent problem-solving skills and the ability to work independently and as part of a team.

We are seeking a highly skilled LLM Pre-training & Distributed Systems Engineer. This role is essential for orchestrating large-scale machine learning training runs and optimizing  distributed infrastructure. The ideal candidate will have a deep understanding of GPU clusters and extensive experience in system engineering to ensure efficient and reliable training processes.

Responsibilities:

• Orchestrate distributed training runs across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM.
• Optimize networking (InfiniBand/RDMA) and memory management to prevent out-of-memory errors.
• Automate checkpointing and failure recovery during month-long training runs.

Required Skills:

• Deep expertise in 3D parallelism (Data, Tensor, Pipeline).
• Experience managing SLURM or Kubernetes-based GPU clusters.
• Strong systems engineering background (C++, CUDA, Python).

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

As the Associate Director, MLOps Lead, you will lead the team responsible for the backbone of our AI/ML Stack: the infrastructure that bridges ML research and massive-scale production. Your primary directive is to evolve our stack to meet the next scale of needs in large scale ML training & inference workloads.  

You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better – without taking yourself too seriously. Our technical space is broad: high-scale AI training & inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and a bit of everything in between.

What You’ll Do

This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives. 

• Vision and Roadmap: Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units. Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth. 
• Team Management: Lead and mentor a team of 6-7+ high-performing engineers. Strategically allocate resources to manage support for existing services while executing key strategic initiatives.
• Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions.
• Foundation Model Readiness: Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency.
• Inference Modernization: Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments.
• System Observability: Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution.
• Technology Refresh: Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs.

What You Bring

To be successful in this role with us, you'll at least need:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 2-3+ years of experience managing engineering team(s), with a focus on building production-grade frameworks for MLOps or ML Infrastructure.
• Deep technical expertise with ML workloads on kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents) and DevOps principles and infrastructure-as-code (Helm, Terraform).
• Proven experience managing petabyte-scale datasets and high-throughput production inference pipelines.
• Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture.
• Use of AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycle.

It Would Be Great If You Also Have

• Exposure to ML frameworks like PyTorch or Scikit-learn.
• Experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR)
• Expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML.
• Familiarity with security and compliance best practices in ML systems.

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 

PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.

The cash compensation outlined below includes base salary or hourly wage and on-target commission for employees in eligible roles. The summary below indicates if an employee in this position is eligible for annual bonus, overtime pay and equity awards. Individual compensation packages are tailored based on skills, experience, qualifications, and other job-related factors. 

Annual Pay Range:
AD, MLOps: $181,500 - $278,300

Not Overtime Eligible

Eligible for Equity

Role overview

The Manager, Data Science will lead an Inventory & Dealer Data Science team focused on developing, deploying, and optimizing machine learning models that power CarGurus’ products and business insights. Sitting within the broader Data Science organization, this team is responsible for modeling automotive marketplace dynamics and dealer behavior. The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production. This leader is accountable for the team’s strategic direction and impactful delivery, while building a culture of innovation, technical excellence, and collaboration. The Manager partners with Product, Engineering, Analytics, and cross-functional stakeholders to ensure team efforts advance CarGurus’ goals in intelligence-powered products.

What you'll do

• Oversee the development, training, and evaluation of machine learning models covering Inventory and Dealer intelligence. Leverage modeling techniques including recommendations, demand forecasting, churn risk prediction, and valuation algorithms.
• Facilitate experimentation, A/B testing, and measurement of production models, ensuring robust evaluation of business impact
• Build and develop a high-performing team - providing regular coaching, feedback, and performance assessments; ensure equitable access to growth opportunities.
• Drive innovation, continuous improvement, and adoption of best-in-class ML and AI practices. Champion best practices across the organization.
• Participate in roadmap planning, aligning the team’s work with broader business priorities and long-term strategy. Flag resource constraints and escalate tradeoff decisions when needed.
• Act as a visible, accessible leader - communicating objectives, updates, risks, and wins to all stakeholders.
• Partner with peers in the Machine Learning Platform, Product and Data teams, and other stakeholders to facilitate effective cross-team working relationships and represent data science in cross-functional forums.

What you'll bring

• 5+ years of experience in a data science or machine learning engineering role
• 2+ years of people leadership experience
• Deep expertise in machine learning models and their application to classification, regression, ranking, and recommendation problems. Familiarity with standard evaluation metrics and statistical best practices.
• Experience deploying and owning models in production and a working understanding of MLOps practices.
• Strong working knowledge of Python development best practices and the Python ML ecosystem (e.g., scikit-learn, PyTorch, XGBoost, numpy, pandas).
• Experience with cloud platforms, particularly AWS, including deploying, monitoring, and troubleshooting ML models.
• Effective communication and facilitation abilities with both technical and non-technical stakeholders (including data scientists, product management, senior leadership, and engineers).
• Experience contributing to the technical direction for a team and a strategic mindset for prioritizing projects and aligning them with business goals.
• Knowledge of standard software development methodologies and a history of driving continuous improvement.
• Experience in a collaborative, cross-functional environment, with the ability to build effective working relationships and foster an inclusive team culture.

About Rhino Federated Computing

Rhino Federated Computing Rhino solves one of the biggest challenges in AI: seamlessly connecting siloed data, apps, and models through federated computing. Our platform enables our customers to share business insight and processing without compromising intellectual property, data privacy, or data security. Our Rhino Federated Computing Platform offers flexible architecture across multi-cloud and on-prem hardware, end-to-end data management controls and workflows, and privacy enhancing technologies. Rhino is trusted by over 60 leading consortia and enterprises worldwide, including 14 of 20 of Newsweek’s ‘Best Smart Hospitals’ and top 20 global biopharma companies.

The company is headquartered in Boston, with an R&D center in Tel Aviv.

About the Role

As the Team Lead for the Forward Deployed AI Engineering Team, you will serve as a hands-on Player/Coach, leading a customer-facing AI engineering and scientist group responsible for deploying and scaling Rhino’s Federated Computing Platform (FCP) in complex, real-world environments. You will maintain a direct, hands-on contribution by working with customers’ technical and research teams to design, implement, and optimize federated AI models and systems. Critically, you will also coach, mentor, and manage a team of applied AI engineers, guiding them through federated model training, inference, deployment, and monitoring, while helping customers unlock the full potential of federated data collaboration. Your leadership will shape how federated AI is operationalized at scale. 

Key Responsibilities

Technical Leadership & Delivery

• Lead the design and delivery of federated AI solutions across imaging, text, and structured data domains.
• Design and implement federated training and inference pipelines, integrating data preprocessing, model orchestration, and AIOps tooling.
• Own project execution from scoping to deployment — ensuring clarity, speed, and technical excellence.

Customer Engagement & Enablement

• Partner directly with customers to understand workflows, map data ecosystems, and design scalable AI solutions.
• Serve as a trusted technical advisor on privacy-preserving ML, model optimization, and deployment best practices.
  Build long-term relationships that drive adoption and measurable outcomes for customer programs.

Team Leadership 

• Manage multiple concurrent engagements, balancing strategic leadership with hands-on problem-solving.
• Provide mentorship to Forward Deployed Engineers and Scientists, fostering a high-performance, learning-driven culture.

Product Feedback & Innovation

• Identify reusable solution patterns and contribute to the standardization of federated AI workflows.
• Surface customer insights and field feedback that shape Rhino’s roadmap.
• Collaborate with product and research teams to prototype and validate new federated learning research and frameworks.

Required Skills

• Experience: 5+ years in applied AI, machine learning engineering, or MLOps roles; experience deploying models in production environments.
• Technical Expertise: Deep hands-on experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and AI/MLOps ecosystems.
• Generative AI Tools: Familiarity with LLM frameworks (GPT, Gemini, Claude, LangChain, agentic frameworks, etc.).
• Programming & Systems: Strong proficiency in Python, REST APIs, and data systems (SQL/NoSQL); comfort with cloud computing and GPU-based systems.
• Delivery Focus: Proven ability to own technical outcomes end-to-end, from scoping and prototyping to full-scale delivery.
• Collaboration: Excellent communication skills and the ability to operate across customer, product, and engineering teams.
• Mindset: Action-oriented, adaptable, and able to prioritize effectively in fast-changing environments.

Preferred Skills

• Industry experience in healthcare, biopharma, financial services, and public sector.
• Ph.D. or M.S. in a quantitative field (Computer Science, Engineering, Biomedical Informatics, Bioinformatics, Chemistry, or related).
• Prior experience deploying federated learning, privacy-preserving AI, or secure distributed computing systems.

Location 

Boston preferred, but open to other locations for highly qualified candidates.

Visa Sponsorship

We will sponsor visas for USA PhD candidates in the relevant areas - Mathematics, Artificial Intelligence, Federated Learning, Computational Chemistry/Biology/Biomedical candidates. An B.S./M.S. with extensive, directly relevant industry experience will also be considered.

Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.

Key Responsibilities:

• Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
• Independently lead small research projects while collaborating with team members on larger initiatives
• Design, run, and analyze scientific experiments to advance our understanding of large language models
• Optimize and scale our training infrastructure to improve efficiency and reliability
• Develop and improve dev tooling to enhance team productivity
• Contribute to the entire stack, from low-level optimizations to high-level model design

Qualifications:

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
• Strong software engineering skills with a proven track record of building complex systems
• Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
• Familiarity with large-scale machine learning, particularly in the context of language models
• Ability to balance research goals with practical engineering constraints
• Strong problem-solving skills and a results-oriented mindset
• Excellent communication skills and ability to work in a collaborative environment
• Care about the societal impacts of your work

Preferred Experience:

• Work on high-performance, large-scale ML systems
• Familiarity with GPUs, Kubernetes, and OS internals
• Experience with language modeling using transformer architectures
• Knowledge of reinforcement learning techniques
• Background in large-scale ETL processes

You'll thrive in this role if you:

• Have significant software engineering experience
• Are results-oriented with a bias towards flexibility and impact
• Willingly take on tasks outside your job description to support the team
• Enjoy pair programming and collaborative work
• Are eager to learn more about machine learning research
• Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects
• Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
• View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights
• Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.

Sample Projects:

• Optimizing the throughput of novel attention mechanisms
• Comparing compute efficiency of different Transformer variants
• Preparing large-scale datasets for efficient model consumption
• Scaling distributed training jobs to thousands of GPUs
• Designing fault tolerance strategies for our training infrastructure
• Creating interactive visualizations of model internals, such as attention patterns

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

If you're excited about pushing the boundaries of AI while prioritizing safety and ethics, we want to hear from you!

AI Technical Lead

Requires minimum of 2 days a week onsite in Boston, MA office

$160,000 - $200,000 + bonus

About the Role

IANS is rapidly expanding its AI strategy toward a Data-as-a-Service (DaaS) model—delivering not only insights through our own applications, but also structured data feeds, APIs, and AI-ready interfaces that enable clients to build their own intelligent systems and agentic workflows.

We are seeking a highly skilled AI Technical Lead to drive the design, development, and evolution of both our internal AI systems and our client-facing agent platforms. This role serves as the technical authority across AI initiatives—architecting agentic systems, building reusable frameworks, and guiding the engineering strategy that powers IANS’ next generation of data-driven products.

You will own the full lifecycle of AI and ML systems, with deep expertise in LLMs, retrieval-augmented generation (RAG), agent design, and enterprise-grade architecture.

AI Architecture, Agent Systems & DaaS Platform Development

• Lead the design and development of AI agent systems aligned with IANS’ evolving DaaS strategy, enabling clients to consume IANS data feeds and build their own AI agents using proprietary, internal, and third-party data.
• Architect agentic workflows that orchestrate retrieval, reasoning, tool use, planning, and action execution across enterprise contexts.
• Develop reusable agent frameworks, primitives, and SDK-style abstractions that external clients can safely and flexibly extend for their own applications.
• Integrate RAG pipelines, tool use, APIs, and multi-agent patterns into robust, production-grade platforms.
• Define scalable abstractions for agent memory, tool orchestration, context management, and autonomous task execution.

Technical Leadership & Strategy

• Shape the long-term AI and agent-platform roadmap in partnership with product and engineering leaders.
• Serve as the primary subject-matter expert on agentic systems, LLM architectures, RAG, and DaaS platform design.
• Provide technical leadership and mentorship across AI initiatives, ensuring consistent engineering excellence.
• Evaluate emerging technologies, frameworks, and standards relevant to AI agents, data delivery, automation, and platform extensibility.

Software Engineering & Systems Integration

• Develop production-grade Python code using frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, and modern agent toolkits.
• Design and maintain APIs, microservices, and developer-facing interfaces that enable both internal and external teams to integrate with IANS’ data and agent platforms.
• Implement robust testing, observability, logging, and performance optimization for complex agent workflows and model-driven systems.
• Ensure compatibility between internal systems and externally consumed SDKs, tools, and data interfaces.

AI Infrastructure, Deployment & MLOps

• Architect and manage scalable cloud-based AI/agent infrastructure (Azure preferred).
• Implement MLOps pipelines for continuous delivery, safe model updates, monitoring, evaluation, and governance.
• Build and maintain data pipelines supporting RAG, retrieval, embeddings, agent memory, and inference.
• Establish observability frameworks for agent behavior, tool interactions, and cost management.

Security, Governance & Enterprise-Grade Standards

• Define and enforce strict standards for security, data isolation, governance, observability, and cost control, especially when exposing agent capabilities and data products to clients.
• Ensure safe sandboxing, permissioning, and policy enforcement for client-built AI agents.
• Oversee compliance with regulatory frameworks (GDPR, CCPA, etc.) and responsible AI practices including fairness, explainability, privacy, and risk mitigation.

Qualifications & Skills

Required

• 5+ years of experience in AI/ML engineering, with 2+ years in a senior or lead role.
• Hands-on experience designing agentic AI systems, AI agents, or multi-agent frameworks for production environments.
• Deep expertise with LLMs, NLP, RAG pipelines, embeddings, and generative AI technologies.
• Strong Python engineering skills; experience with PyTorch, TensorFlow, Hugging Face, LangChain, etc.
• Experience architecting APIs, SDKs, or developer-facing tools used by external customers.
• Strong understanding of distributed systems, cloud infrastructure (Azure preferred), and scalable architectures.
• Familiarity with MLOps workflows, CI/CD, model monitoring, evaluation, and lifecycle management.
• Strong communication, leadership, and cross-functional collaboration skills.

Nice to Have:

• Experience with LoRA, PEFT, RLHF, and advanced fine-tuning techniques.
• Background in data engineering supporting model training and retrieval.
• Experience with Docker, Kubernetes, or large-scale microservice architectures.
• Experience applying ethical or responsible AI practices in enterprise contexts.

Why Join Us?

• Shape an industry-leading AI and DaaS platform powering the next generation of cybersecurity insights.
• Lead the architecture of agentic systems used by both internal teams and Fortune-class clients.
• Work with a highly skilled, collaborative team building forward-looking AI products.
• Competitive compensation, benefits, and opportunities for growth.

About the Role:

We are seeking a high-impact Machine Learning Developer/Engineer to join our integrated discovery team. In this role, you will be the algorithmic engine of our programs, developing and deploying state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core scientific partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.

Key Responsibilities:

• Implement algorithms for hit identification through virtual screening and other high throughput computational methods as part of a cross-functional team
• Adapt and implement cutting-edge ML architectures for co-folding to augment our extensive internal structural biology expertise and capabilities
• Design and deploy active learning frameworks that utilize experimental assay results to iteratively improve model performance and reduce the number of "Design-Make-Test-Analyze" cycles leveraging state-of-the-art de novo design, ADMET predictions, and affinity predictions

Required Qualifications:

• Ph.D. preferred in Computer Science, Machine Learning, Engineering or a related field, or BS/MS + seasoned experience
• Proven experience with protein-ligand co-folding algorithms (e.g., Boltz, AlphaFold, OpenFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines.
• Advanced proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be capable of building and maintaining production-quality code and data pipelines.
• Exceptional ability to communicate the "why" behind a design to a diverse scientific audience.

Preferred Qualifications:

• Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
• Expertise fine-tuning existing models with internally generated structural biology and biology data
• Experience deploying ML/AI algorithms for use by a cross-functional scientific audience
• 1-4+ years of experience in a biotech or pharma setting

Skills & Competencies:

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems including tools for hit identification (virtual screening, HTS)
• Expert level use of protein-ligand co-folding algorithms to small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold)   
• Experience writing production-level code for ML tasks:
•  Knowledge of key scientific packages (RDKit, scikit-learn, numpy, pandas, pytorch, deepchem, polars, PyG/DGL):
• Write robust, testable, and version-controlled code that adheres to CI/CD and data governance best practices.
• Value clarity, documentation, and structured thinking, especially when working with complex data
• Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale

Trivelta builds the technology that powers modern, social-first gaming experiences. Through our proprietary sweepstakes-based sportsbook and casino engine, we enable partners to launch their own fully branded, legally compliant gaming products. Our flagship consumer app, ReBet, demonstrates the power of the Trivelta platform—combining real social interactions, predictive gameplay, and casino entertainment in one unified experience.

Headquartered in Boston with operations in Monterrey, Barcelona, and Atlanta, we’re scaling rapidly and building a team passionate about redefining how people play, bet, and connect online.

This position is hybrid out of our Boston HQ located next to South Station.

Position Overview:

We are seeking a Principal Quant Engineer to work in close collaboration with Research, Portfolio Management, and Data teams to design and implement scalable, production-grade platforms that support Acadian’s quantitative research and investment processes.

This role focuses on building quant tooling that enables researchers to efficiently develop, evaluate, and deploy quantitative models across investment processes including alpha, risk, transaction cost analysis, attribution, etc. The emphasis is on creating infrastructure that makes quantitative research repeatable, observable, and production-ready at scale.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do:

• Design and implement quant tooling that supports signal construction and evaluation workflows, powering production portfolios across global asset universes.
• Work closely with quantitative researchers to ensure research infrastructure continues to evolve in step with their needs, enabling efficient experimentation and reliable alpha generation.
• Lead the AI centric development and evolution of core platform capabilities that support signal computation, data access, workflow orchestration, and distributed execution for both quantitative signals and machine learning-based techniques, including deep learning workflows built with PyTorch.
• Ensure platform flexibility while maintaining integration with downstream processes for portfolio construction, portfolio management, marketing, client reporting, etc
• Incorporate machine learning and AI techniques into signal construction, parameter estimation, and efficacy evaluation workflows where they improve forecast quality and research productivity, including hyper‑parameter optimization using frameworks such as Optuna or similar.
• Provide architectural leadership to improve robustness, transparency, scalability, and performance of data- and compute-intensive research and production workflows.
• Collaborate with data and platform teams to streamline access to market data, alternative datasets, and compute resources, reducing friction in the research “inner loop” of signal development, backtesting, and validation.
• Contribute to the end‑to‑end machine learning lifecycle capabilities, including experiment tracking, model management, embedding and graph visualization, and deployment, drawing on tools and concepts such as MLflow.

We’re Looking for Teammates With:

• Bachelor’s degree or higher in Computer Science, Engineering, Applied Mathematics, or a related quantitative field.
• 7+ years of experience in software engineering, with significant hands-on work supporting quantitative research, signal development, or systematic investing in production environments.
• Demonstrated experience building and operating quantitative platforms that support signal modeling, evaluation, and deployment at scale.
• Strong proficiency in Python and modern data processing and numerical computing tools (e.g., Polars).
• Familiarity with machine learning and AI-assisted development techniques, including the use of AI or agentic coding tools (e.g. Claude Code) to improve research and engineering productivity.
• Solid understanding of system design concepts.
• Demonstrated ability to deliver robust, transparent, and performant systems.
• Strong attention to detail and a commitment to producing accurate, reliable, and well-engineered solutions.
• Creativity and enthusiasm for solving complex problems.
• Strong work ethic and a roll-up-your-sleeves mindset, with the ability to operate effectively in a hands-on, collaborative environment.

The base salary range for this role is $185,000 – $225,000 per year. Actual compensation will be determined based on a candidate’s skills, qualifications, and relevant experience. In addition to base pay, this position may be eligible for discretionary incentive compensation and includes participation in Acadian’s comprehensive benefits program, which includes health, retirement, and wellness offerings.

About the Role:

We are seeking a high-impact Machine Learning Scientist to join our integrated discovery team. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward Go/No-Go decision points and candidate nomination.  This role may be responsible for the management and development of individual team members.

Key Responsibilities:

• Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
• ML lead on project teams, collaborating intimately with medicinal chemists to refine SAR and with structural biologists to integrate co-folding and structure-based insights into ML workflows
• Data-Driven Decision Making: Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
• May be responsible for management and development of internal team members

Required Qualifications:

• Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
• Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
• 4-7+ years of experience in a biotech or pharma setting performing ML support for small molecule drug discovery with clear evidence of impact on drug discovery programs
• Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly led to the achievement of program milestones
• Expert proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be able to build and maintain production-quality code and data pipelines

Preferred Qualifications:

• Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
• Expertise fine-tuning existing models with internally generated structural biology and biology data
• Expert-level knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context

Skills & Competencies:

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
• Demonstrated expertise using small molecule drug discovery ML/AI tools (AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)  
• Expert level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
• Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience
• Experience mentoring and developing teams

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

Here’s a collection of articles about our product, mission, recent funding round, etc.

Job Description

We’re hiring an exceptional data scientist to join our team (Boston or NYC office). In this role, you will be responsible for advancing our applied ML and data science capabilities for healthcare. 

You can expect to:

• Develop, validate, and measure the impact of large language model (LLM) and other ML-based approaches for chart abstraction from both unstructured and structured healthcare data.
• Apply and rigorously evaluate cutting-edge methods to solve real clinical problems.
• Work hand-in-hand with our customers to best leverage Layer Health’s platform and supercharge their workflows. 
• Stay up-to-date on the latest in applied NLP and generative AI techniques and proactively explore these technologies where applicable.
• Establish and enforce best practices for data science and analytics.
• Cultivate and foster a robust and thoughtful data science and product culture that drives the company forward.

We look for:

• 5-7+ years of professional experience as a data scientist working for a company with major ML dependencies.
• At least 3-4 years of healthcare / clinical applications experience.
• A solid foundation of ML/methods.
• Fluent programming skills in Python, and fluency with modern data science and ML/NLP libraries (PyTorch, Tensorflow, HuggingFace, etc.). 
• Deep fluency and instincts for data manipulation, treatment, and evaluation, with the ability to wrangle large, complex datasets efficiently and methodically.
• Familiarity with modern applied LLM techniques and their practical implementations (any experience using these techniques is a bonus).
• Proactive mindset to identify and solve problems, continuously improving our data science capabilities.
• A strong communicator who thrives in a customer-focused, fast-paced environment.
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
• We are a Boston-based company, and expect engineers to meet regularly in-person in Boston (engineers from Boston, NYC, or east coast are welcome).

Expected compensation range for this role is $180,000 - 200,000. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Join us and help us transform healthcare with AI. Layer Health is committed to foster an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law. 

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

We’re seeking outstanding hires to join our team as early members. This is an opportunity to contribute to a high-impact, collaborative, mission-driven team, and help define the next stage of growth for Layer Health. Together, we will create the AI layer that will redefine healthcare for the better.

Here’s a collection of articles about our product, mission, recent funding round, etc.

Job Description

We’re hiring an exceptional ML scientist. In this role, you will be responsible for pioneering innovative machine learning techniques to advance our fundamental clinical machine learning and large language model efforts.

You can expect to:

• Design and implement state-of-the-art machine learning techniques to advance Layer Health’s research agenda (in areas such as information extraction, multimodal reasoning, and summarization).
• Propose new agentic methods that tackle fundamental NLP and ML challenges such as modeling over multiple documents, long contexts, multiple modalities, and with limited or noisy labels  
• Build foundation models to power the future of clinical information extraction & synthesis, from training through inference. 
• Stay up-to-date and actively engage with cutting-edge research in NLP, generative AI, and clinical machine learning.
• Collaborate with the broader engineering team to ship performant products that meet user needs.
• Cultivate and foster a robust and thoughtful R&D culture that drives the company forward.

We look for:

• Exceptional methodological research background and experience, including but not limited to:
• A PhD in computer science/applied mathematics or equivalent research experience, specializing in natural language processing and machine learning.
• High-impact, early-author publications at top peer-reviewed ML journals/conferences.
• Demonstrated record of delivering real-world impact from start to finish– with the ability to design, develop, and ship innovations.
• Strong programming skills and fluency with modern machine learning/LLM stacks (deep learning libraries e.g. PyTorch, Jax).
• Past experience in training/inference of foundation models (billions of parameters, distributed training, familiarity with state-of-the-art techniques).
• A strong communicator who thrives in a customer-focused, fast-paced environment.
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
• Past experience in healthcare of life sciences is a plus, but not required.
• We are a Boston-based company, and expect employees to meet regularly in-person in Boston (employees from Boston, NYC, or east coast are welcome).

Expected compensation range for this role is $200,000-250,000, in addition to stock options. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Layer Health is committed to fostering an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law.

Join us and help us transform healthcare with AI.