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.  

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

At Klaviyo, we believe the future of software lies not in productivity tools for human users but in software that can run and optimize itself based on outcome or reward metrics. We’ve built the infrastructure and application that serve as the interface between businesses and consumers. We now have over 167,000 customers, billions of consumer profiles, and hundreds of billions of customer messages and follow-on conversion data. We are building state-of-the-art AI and machine learning technologies at Klaviyo to power marketing agent, service agent, and personalization products such as smart send time, audience optimization and product recommendations.

As a Machine Learning Platform Engineering Manager, you will lead a team of engineers building the core platforms, tools, and infrastructure that ML and AI engineers rely on to create advanced agents and models powering Klaviyo’s products. Your team will own platforms for training and deploying models and scaling systems that must operate reliably at Klaviyo’s data and traffic scale.

This is a backend-heavy, hands-on technical leadership role. You will manage and grow engineers, shape the technical roadmap for ML Platform, and collaborate closely with product, ML, data science, and infrastructure partners to deliver durable, high-leverage platforms that accelerate AI development across Klaviyo.

This role is hybrid and based in Boston.

How You’ll Make an Impact

• Enable AI / ML teams across Klaviyo to ship new models and ML-powered features faster, more safely, and more reliably.
• Evolve our ML and LLM platform architecture so teams can build, evaluate, and operate production ML systems with consistent patterns and best practices.
• Improve reliability, observability, and cost efficiency of ML training, inference, and other ML workloads that serve features like smart send time, audience optimization and product recommendations.
• Raise the bar on engineering and operational excellence for systems that support 167K+ customers and billions of events.
• Build and grow a high-performing, inclusive engineering team that operates with ownership, curiosity, and strong customer focus.

What You’ll Do

• Lead, coach, and grow a team of software / ML platform engineers (typically 6–10+ direct reports).
• Set clear goals, provide regular feedback, and support career development aligned with Klaviyo’s Engineering Career Architecture.
• Stay hands-on with the technology: participate in design reviews, code reviews, and occasionally ship code for critical paths or prototypes.
• Partner with product and technical leaders to define and evolve the ML Platform strategy and roadmap, balancing short-term delivery with long-term platform investments.

Learn more about the team’s work in our blogs on Ray Jobs & Ray Tune.

Who You Are

We encourage candidates to apply even if they do not meet all the qualifications listed. ML Platform and MLOps are rapidly evolving spaces and we are all constantly learning.

• 7+ years of experience as a software engineer, ML platform engineer, or similar, building backend or distributed systems.
• 2+ years of experience in a formal people leadership role
• Demonstrated experience launching and operating AI/ML systems in production at scale
• Strong proficiency in Python (or a similar backend language such as Go, Java, or Scala).
• Experience with cloud infrastructure and platform fundamentals (AWS, kubernetes):
• Familiarity with data-intensive systems: streaming or batch processing, data modeling, and designing systems with large-scale data in mind (spark, ray-data).
• Comfortable collaborating with cross-functional technical partners (ML, product, infra, security).
• Strong communication skills and a track record of driving clarity and alignment across teams.

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 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.

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:

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.

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.

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.

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.

About the role:

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.

Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

What you’ll do:

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system

What success looks like:

• You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
• ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
• Model deployments become more secure, observable, and supportable across real-world mission environments
• The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership

Clearance Requirements:

• Ability to obtain and maintain a Top Secret clearance 

Work Type: 

• Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
• May require up to 40% travel
  

Salary Range: $150,000.00 - $200,000.00

What we will offer you:

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO + 11 paid holidays
• Education & training benefits
• Annual budget for your tech/gadgets needs
• Monthly box of yummy snacks to eat while doing meaningful work
• Remote, hybrid, and flexible work options
• Team off-site in fun places!
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession: 

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there? 

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration. 

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.

About the role:

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.

Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

What you’ll do:

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system

What success looks like:

• You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
• ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
• Model deployments become more secure, observable, and supportable across real-world mission environments
• The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership

Clearance Requirements:

• Ability to obtain and maintain a Top Secret clearance 

Work Type: 

• Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
• May require up to 40% travel
  

Salary Range: $150,000.00 - $200,000.00

What we will offer you:

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO + 11 paid holidays
• Education & training benefits
• Annual budget for your tech/gadgets needs
• Monthly box of yummy snacks to eat while doing meaningful work
• Remote, hybrid, and flexible work options
• Team off-site in fun places!
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession: 

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there? 

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration. 

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.

About the role:

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.

Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

What you’ll do:

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system

What success looks like:

• You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
• ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
• Model deployments become more secure, observable, and supportable across real-world mission environments
• The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership

Clearance Requirements:

• Ability to obtain and maintain a Top Secret clearance 

Work Type: 

• Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
• May require up to 40% travel
  

Salary Range: $150,000.00 - $200,000.00

What we will offer you:

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO + 11 paid holidays
• Education & training benefits
• Annual budget for your tech/gadgets needs
• Monthly box of yummy snacks to eat while doing meaningful work
• Remote, hybrid, and flexible work options
• Team off-site in fun places!
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession: 

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there? 

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration. 

We are seeking a skilled MLOps & Agentic Platform Engineer. This role involves managing model registries, developing continuous training loops, and implementing A/B testing infrastructure. The ideal candidate will have a strong DevOps/MLOps background and be adept at deploying scalable microservices and building observability dashboards.

Responsibilities:

• Manage model registries, continuous training loops, and A/B testing infrastructure.
• Deploy agents as scalable microservices on Kubernetes.
• Build observability dashboards to track token usage, latency, and agent reasoning paths.

Qualifications:

• Strong DevOps/MLOps background (Kubernetes, Docker, Terraform).
• Experience with MLflow, Weights & Biases, or LangSmith.
• Knowledge of building scalable microservice architectures.

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 

We are seeking Machine Learning Engineers (Applied Research & Model Development) to join our team and tackle unique machine learning challenges to advance medicine and improve patient care. You will work closely with teams across biomedical data science, product development, translational research, MLOps, and platform engineering to develop and deploy machine learning models for our AI products and services.

What You’ll Do

As an MLE (Applied Research & Model Development) at PathAI, your responsibilities will grow in scope as you progress through levels:

• Design, develop, and deploy machine learning models for research and product development projects.

• Collaborate cross-functionally with scientists, engineers, and product teams to translate biological and clinical requirements into scalable ML solutions.

• Contribute to experimental design and analysis, including ideation, documentation, and reporting.

• Participate in knowledge sharing and team initiatives (e.g., design reviews, journal clubs, ML best practices, governance activities).

• Improve ML pipelines and infrastructure in partnership with MLOps and platform teams.

• Publish and present scientific work, supporting abstracts, manuscripts, and conference contributions.

Level-specific expectations:

• MLE I: Contribute to projects with guidance, implement models, and learn best practices.

• MLE II: Independently deliver on projects, improve processes, and mentor junior engineers.

• MLE III: Lead initiatives end-to-end, set technical direction, and identify new opportunities with clear business and scientific impact.

You will have the opportunity to work in a company where all employees put patients first. We believe that every team member provides valuable contributions to our success, and no task is too small for anyone if it's important to our company goals.   Every PathAI employee is a contributor to our mission to pioneer better patient care by providing the best, most innovative AI tools to biotech, pathologists, clinicians and healthcare organizations. You will work alongside and with leading innovators in the field of AI and medicine and you will play a critical role in product development to impact patient outcomes.

What You Bring

We welcome applicants across all MLE levels. Minimum qualifications differ by level:

MLE II:

• Master’s degree plus 2–4 years of experience, or Ph.D. with 0–2 years of experience.

• Proven track record of developing and deploying machine learning models into production or research applications.

• Strong proficiency in Python, ML frameworks, and data pipeline development.

• Demonstrated ability to work independently on projects, contribute to experimental design, and improve ML workflows.

• Strong communication skills and ability to collaborate across scientific and engineering teams.

MLE III 

• Master’s degree plus 5+ years of experience, or Ph.D. with 3+ years of experience.

• Deep expertise in ML, computer vision, or biomedical AI, with a history of high-impact contributions (publications, open-source, or products).

• Mastery of ML frameworks, software engineering best practices, and deployment pipelines.

• Ability to lead end-to-end projects, mentor others, and set technical direction.

• Experience articulating technical improvements into business or clinical impact.

• Strong record of contributions to scientific strategy (abstracts, manuscripts, conference presentations).

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:
Machine Learning Engineer II: $107,250 - $164,450
Machine Learning Engineer III: $130,500 - $200,100

Not Overtime Eligible

Eligible for Equity

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.

About Rhino Federated Computing

Rhino Federated Computing Rhino solves one of the biggest challenges in AI: seamlessly connecting siloed data through federated computing. Our platform leverages federated computing to connect disparate data sources, enabling secure global collaboration for data pre-processing, AI model training and deployment, without compromising privacy, intellectual property, or data security. Our platform allows for the secure deployment of custom code and third party applications via persistent data pipelines. To do this, Rhino FCP offers flexible architecture across multi-cloud and on-prem hardware, end-to-end data management workflows (multimodal data, schema definition, harmonization, and visualization), and privacy enhancing technologies (e.g., differential privacy). Rhino is trusted by over 60 leading organizations worldwide - including 14 of 20 of Newsweek’s ‘Best Smart Hospitals’ and Top 20 global biopharma companies. Our vertical-agnostic infrastructure is also a foundation for financial services, eCommerce, and beyond.

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

About the Role

We are seeking a highly motivated, detail-oriented, technology-savvy, and creative Senior Product Manager to join our team in the United States. Reporting directly to the VP Product, you will be instrumental in shaping the future of the Rhino Federated Computing Platform.

In this critical role, you will work closely with our Engineering and UX teams to elevate the end-to-end platform experience, spanning the Graphical User Interface (GUI), Software Development Kits (SDKs), and external APIs.  You will be instrumental in defining and delivering the product roadmap with a strong emphasis on platform interoperability and extensibility, assisting quarterly planning cycles, and defining innovative new product features that address the complex needs of our enterprise customers in healthcare, life sciences, and financial services. You will own the user journey from initial data federation to the deployment of production-ready AI agents.

This is a unique opportunity to make a significant impact in a fast-paced, successful startup environment, directly contributing to a platform that is breaking down data barriers and accelerating AI innovation in critical industries.

Key Responsibilities

• Collaborate closely with the VP Product on overall product strategy, roadmap prioritization, and market analysis.
• Define detailed product requirements, user stories, and acceptance criteria for both technical (API/SDK) and end-user (GUI) use cases, ensuring a cohesive experience across all platform surfaces.
• Define requirements for deep-seated AI agents and manage integrations with cloud AI/ML services and compatible with the Model Context Protocol (MCP) to deepen platform analytical capabilities and streamline end-to-end federated workflows.
• Manage and prioritize the product backlog, ensuring alignment with the product roadmap and business objectives. Conduct market research, competitive analysis, and gather customer feedback to identify opportunities and inform product decisions.
• Effectively lead and facilitate agile ceremonies, including sprint pre-planning, sprint planning, daily stand-ups, and sprint retrospectives, to optimize team collaboration and efficiency.
• Collaborate with client-facing teams, providing product materials and expertise. Translate field insights and customer needs into impactful platform improvements and new features, acting as the customer and end-user advocate to Engineering.
• Champion the user experience and UX design, working closely with design resources to create powerful interfaces and seamless developer experiences that simplify the management of the AI lifecycle.
• Define and track key product metrics, analyze performance data, and leverage insights to inform product iterations and strategic decisions.

Required Qualifications

• 4+ years of product management experience in B2B SaaS building AI/ML/Data platforms, with a total of 8+ years of experience in these fields.
• Proven ability to own product areas, define roadmaps, and drive product development from conception to launch within an agile environment.
• Strong understanding of deep tech AI/ML product development with the ability to engage in technical discussions with data scientists.
• Proven experience with user experience (UX) principles and collaborating with UX designers.
• Excellent written and verbal communication skills and exceptional attention to detail.
• Experience working in a startup or fast-growing technology company.
• Based in Boston, MA, USA (hybrid work environment).

Preferred Qualifications

• Applied experience working with Agentic AI workflows, platform integrations, and/or MLOps products.
• A proven track record of delivering highly usable and intuitive enterprise platform experiences (GUI, SDKs, APIs) that simplify complex technical workflows, demonstrating a balance of user-centered design and technical performance.

CSQ227R88

Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. Candidates with an active Secret or higher clearance are strongly encouraged to apply.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ127R310

Candidates located anywhere within the Central or Eastern Time Zones are also eligible for consideration for this role.

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ127R319

As a Resident Solutions Architect (RSA) on our Professional Services team, you will work with customers on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• Handle a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues
• Collaborate with the Databricks Technical, Project Manager, Architect and Customer teams to ensure the technical components of the engagement are delivered to meet customer's needs
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines
• Documentation and white-boarding skills
• Experience working with clients and managing conflicts
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects
• Ability to travel up to 30% when needed

CSQ127R318

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of  professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 6+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps 
• Design and deployment of performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time
• Databricks Certification

CSQ227R87

Candidates located anywhere within the Central or Eastern Time Zones are also eligible for consideration for this role.

As a Senior Big Data Solutions Architect (Sr Resident Solutions Architect) in our Professional Services team you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• You will work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases
• Work with engagement managers to scope variety of professional services work with input from the customer
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications
• Consult on architecture and design; bootstrap hands-on projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• You will work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

What we look for:

• 9+ years experience in data engineering, data platforms & analytics
• Comfortable writing code in either Python or Scala
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
• Deep experience with distributed computing with Apache Spark™ and knowledge of Apache Spark™ runtime internals
• Familiarity with CI/CD for production deployments
• Working knowledge of MLOps
• Capable of design and deployment of highly performant end-to-end data architectures
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Experience in building scalable streaming and batch solutions using cloud-native components
• Travel to customers up to 20% of the time
• Nice to have: Databricks Certification

Summary

QuEra Computing is seeking a mission-driven Quantum Scientist focused on designing and delivering specialized content for community engagement, customer training, and AI-enabled learning experiences. You’ll use your expertise in quantum information, machine learning, and modern AI systems to create learning pathways that bridge users to the latest outcomes in quantum computing research – particularly that by QuEra’s team – via our Bloqade software suite and our current and upcoming neutral-atom hardware.

In addition to building world-class educational content, you will help integrate AI agents, ML-driven analytics, and scalable infrastructure into our enablement ecosystem—enhancing user onboarding, experimentation workflows, and community engagement.

Deliverables include lessons, tutorials, notebooks, AI-assisted labs, hackathon challenges, interactive agents, and technical presentations tailored to users across academia, national labs, and industry.

Some travel may be required for workshops, conferences, and hackathons.

Responsibilities

• Design, author, and maintain structured, hands-on learning materials (courses, tutorials, notebooks, problem sets, quizzes, and challenge tracks) aligned with  neutral-atom quantum computing capabilities.
• Develop AI-enhanced learning tools, including intelligent tutoring workflows, agent-assisted coding labs, and automated feedback systems.
• Build and deploy lightweight ML or AI agent systems that support user onboarding, documentation navigation, experiment configuration, or workflow optimization.
• Produce clear, accurate technical presentations and demos for customers, partners, conferences, and hackathons; deliver live and online instruction.
• Closely collaborate with the applications research, product and solutions teams to ensure content reflects the latest algorithms, applications, APIs, and hardware constraints.
• Translate new R&D into learner pathways; define objectives and assessments; use learner data, ML-driven insights, and feedback to iterate and improve.
• Contribute to scalable infrastructure for educational environments (cloud-based notebooks, APIs, model endpoints, experiment orchestration systems).
• Drive customer/partner enablement and community programs (hackathons, forums, workshops) and expand documentation to lower adoption barriers.
• Explore and prototype AI agents that assist users in quantum program synthesis, parameter optimization, debugging, or workflow automation.

Qualifications

• Advanced experience in Physics, Computer Science, Electrical Engineering, Machine Learning, or a related field (PhD preferred; MSc/BSc with equivalent experience considered).
• Strong foundation in quantum information/science, spanning algorithmic building blocks, software tooling, data analysis, and hardware limitations.
• Experience using machine learning systems, AI agents, LLM-based tooling, or intelligent automation frameworks.
• Familiarity with modern ML/AI infrastructure (e.g., model APIs, vector databases, agent orchestration frameworks, cloud deployment, experiment tracking).
• Experience creating technical education materials or teaching (tutorials, workshops, developer content), with demonstrated interest in research-informed pedagogy.
• Excellent written and verbal communication skills; ability to explain complex topics to diverse technical audiences.
• Collaborative mindset and the discipline to independently deliver high-quality technical and educational artifacts.

Preferred Qualifications

• Experience with Bloqade or other QIS tooling for scientific computing.
• Familiarity with neutral-atom quantum computing platforms.
• Experience applying ML techniques and agents to scientific and quantum computing, particularly in the context of education.
• Experience building or deploying AI agents (e.g., retrieval-augmented systems, code assistants, orchestration frameworks).
• Experience with MLOps, infrastructure-as-code, containerization, or cloud-based notebook environments.
• Track record presenting at conferences, running hackathons, or building developer communities.
• Comfort using data analytics and ML methods to evaluate learning outcomes and improve curricula.

The approximate base salary range for this position is $130,400 - $211,900.

We consistently monitor external market data and update base salary ranges accordingly.  We determine base compensation decisions on several factors, including as geographic placement, role-specific knowledge, skills, and/or experience.  In addition to our base salary offerings, we also provide equity grants for all new hires.

QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.

#LI-NB1

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.

As an AI Research Scientist III at 1910 you will be expected to roll up your sleeves as an Individual Contributor (IC) by keeping up with relevant scientific literature, prototyping promising methods, and contributing to our active drug design campaigns by applying 1910’s productionized AI/ML models. 

Role Description 

• Propose and prototype AI and Machine Learning solutions that address use cases in 1910’s design pipeline  

• Independently apply productized AI and Machine Learning models to advance 1910’s active drug design campaigns  

• Independently write and publish peer reviewed scientific articles  and mentor junior AI Researchers with their work  

• Periodically presenting recent AI research at internal journal clubs  

• Work cross-functionally with scientific colleagues, being a subject matter expert in how AI and Machine Learning can be used to answer cheminformatics and bioinformatics questions  

• Keep up-to-date on cutting-edge research in the AI for drug discovery space  

• Manage junior AI Research Scientists including having weekly 1:1s, and regular performance reviews  

Qualifications 

• PhD in a relevant discipline (Computer Science, Biology, Chemistry, etc.) 

• 3+ years of relevant industry experience (AI, Drug Discovery, etc.) 

• A strong understanding of statistics and classical machine learning methods (SVM, RF, etc.)  

• Industry experience training and deploying cutting edge deep learning methods (Transformers, Graph Neural Networks, etc.)  

• Wet lab validation of at least 2 AI-driven drug discovery initiatives 

• Demonstrated experience writing clean, maintainable, production-quality Python code, with examples available in code repositories 

• Familiarity with MLOps and DevOps best practices  

• Exposure to distributed computing (Microsoft Azure, HPC Cluster, etc.)  

• Strong track record of publications of individual research in top-tier AI journals and conferences   

• Ability to demonstrate business value related to developed AI and Machine Learning solutions in biology and medicinal chemistry related to your modeling efforts 

• Ability to work effectively in a fast-paced startup environment with evolving projects and shifting priorities driven by business needs 

• Excellent written and verbal communication skills, including the ability to create clear documentation for roadmaps, proposals, and related materials 

Nice to Haves 

• Past experience managing junior AI Research Scientists  

#LI-Onsite

As an AI Research Scientist I at 1910 you will be expected to roll up your sleeves as an Individual Contributor (IC) by keeping up with relevant scientific literature, prototyping promising methods, and contributing to our active drug design campaigns by applying 1910’s productionized AI/ML models.  

Role Description 

• Propose and prototype AI and Machine Learning solutions that address use cases in 1910’s design pipeline  

• Apply productized AI and Machine Learning models to advance 1910’s active drug design campaigns with the support of senior members of the AI Research Team  

• Write and publish peer reviewed scientific articles with the support of senior members of the AI Research Team 

• Periodically presenting recent AI research at internal journal clubs  

• Learn how a molecule processes through the drug discovery process 

• Work cross-functionally with scientific colleagues, being a subject matter expert in how AI and Machine Learning can be used to answer cheminformatics and bioinformatics questions  

• Keep up-to-date on cutting-edge research in the AI for drug discovery space  

Qualifications 

• PhD in a relevant discipline (Computer Science, Biology, Chemistry, etc.) 

• A strong understanding of statistics and classical machine learning methods (SVM, RF, etc.)  

• Experience training and deploying cutting edge deep learning methods (Transformers, Graph Neural Networks, etc.)  

• Demonstrated experience writing clean, maintainable, production-quality Python code, with examples available in code repositories. 

• Familiarity with MLOps and DevOps best practices  

• Exposure to distributed computing (Microsoft Azure, HPC Cluster, etc.)  

• Ability to work effectively in a fast-paced startup environment with evolving projects and shifting priorities driven by business needs 

• Excellent written and verbal communication skills, including the ability to create clear documentation for roadmaps, proposals, and related materials 

Nice to Haves 

• An understanding of how a molecule progresses through the drug discovery process 

• Relevant industry experience via internship and co-op (AI, Drug Discovery, etc.) 

#LI-Onsite

As an AI Research Scientist II at 1910 you will be expected to roll up your sleeves as an Individual Contributor (IC) by keeping up with relevant scientific literature, prototyping promising methods, and contributing to our active drug design campaigns by applying 1910’s productionized AI/ML models. 

Role Description 

• Propose and prototype AI and Machine Learning solutions that address use cases in 1910’s design pipeline  

• Apply productized AI and Machine Learning models to advance 1910’s active drug design campaigns with minimal support from senior members of the AI Research Team 

• Write and publish peer reviewed scientific articles with minimal support from senior members of the AI Research Team 

• Periodically presenting recent AI research at internal journal clubs  

• Work cross-functionally with scientific colleagues, being a subject matter expert in how AI and Machine Learning can be used to answer cheminformatics and bioinformatics questions  

• Keep up-to-date on cutting-edge research in the AI for drug discovery space  

Qualifications 

• PhD in a relevant discipline (Computer Science, Biology, Chemistry, etc.) 

• 2 years of relevant industry experience (AI, Drug Discovery, etc.) 

• A strong understanding of statistics and classical machine learning methods (SVM, RF, etc.)  

• Industry experience training and deploying cutting edge deep learning methods (Transformers, Graph Neural Networks, etc.)  

• Wet lab validation of at least 1 AI-driven drug discovery initiatives 

• Demonstrated experience writing clean, maintainable, production-quality Python code, with examples available in code repositories 

• Familiarity with MLOps and DevOps best practices  

• Exposure to distributed computing (Microsoft Azure, HPC Cluster, etc.)  

• Strong track record of publications of individual research in top-tier AI journals and conferences   

• Ability to demonstrate business value related to developed AI and Machine Learning solutions in biology and medicinal chemistry related to your modeling efforts 

• Ability to work effectively in a fast-paced startup environment with evolving projects and shifting priorities driven by business needs 

• Excellent written and verbal communication skills, including the ability to create clear documentation for roadmaps, proposals, and related materials 

#LI-Onsite