Lead research on post‑training methods for generative robot policies, enabling rapid adaptation and continual learning in real‑world robotic systems.
Compensation
The pay range for this position at commencement of employment is expected to be between $158,400 and $227,700 /year for Massachusetts-based roles.
Responsibilities
Design and implement methods for adapting generative robot policies using reinforcement learning, imitation learning, agentic tool use, and related methods.
Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human‑driven signals.
Develop rollout‑based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on‑policy rollouts, in both simulation and on real robot hardware.
Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
Publish research outcomes and contribute to the open robotics and embodied AI communities.
Requirements
Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
Supports Factory of the Future and Industry 4.0 initiatives by leveraging data, analytics, and AI technologies to improve manufacturing efficiency, quality, and operational performance.
Compensation
$90,000 - $125,000 annual compensation
Responsibilities
Leads industry 4.0 and AI initiatives by identifying, prioritizing, and implementing data-driven solutions that optimize manufacturing processes across molding, assembly, and co-extrusion processes.
Collaborates with cross-functional teams including engineering, operations, IT, and automation teams to integrate digital technologies and analytics into production systems.
Develops and maintains data infrastructure including ETL pipelines, databases, SQL queries, and data environments to support manufacturing analytics and AI applications.
Collects, validates, and manages production data from multiple sources, including PLCs, HMIs, sensors, and enterprise systems, ensuring data accuracy and integrity.
Designs, develops, and optimizes machine learning and AI models to improve quality, productivity, predictive maintenance, process control, and operational efficiency.
Requirements
Bachelor's degree in a relevant field such as Computer Science, Engineering, Data Science, or a related discipline.
Nice to have: familiarity with cloud-based analytics platforms (e.g., AWS, Azure)
Nice to have: familiarity with big data technologies (e.g., Hadoop, Spark)
Sr. AI Engineer
Blue Cross Blue Shield Of Massachusetts · Posted Oct 7, 2026
Lead AI engineering teams at Blue Cross Blue Shield of Massachusetts, driving technical excellence, architecture, and production-grade AI solutions in healthcare.
Locations (2)
Boston, Massachusetts, United States
Hingham, Massachusetts, United States
Compensation
Salary Range: $151,200.00 - $184,800.00
The job posting range is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less or the range may be modified in the future. An employee’s pay position within the salary range will be based on several factors including, but limited to, relevant education, qualifications, certifications, experience, skills, performance, shift, travel requirements, sales or revenue-based metrics, and business or organizational needs and affordability.
This job is also eligible for variable pay.
Responsibilities
Provide technical leadership and mentorship to AI engineers
Lead solution design and implementation of agentic AI systems
Oversee delivery and execution with product owners
Build scalable AI systems and manage performance
Troubleshoot complex problems in ML pipelines and cloud infrastructure
AI Solution Architect
Blue Cross Blue Shield Of Massachusetts · Posted Dec 19, 2025
Design and implement AI agent solutions within the Bluefield framework, collaborating with Generative AI Engineers and ensuring high technical quality.
Compensation
Salary Range: $175,860.00 - $214,940.00
Responsibilities
Design detailed technical solutions and blueprints for individual AI agents
Ensure agent development follows architectural patterns, coding standards, security protocols, and best practices
Optimize Google Agent Space and Google Cloud Platform services for performance, cost-efficiency, and scalability
Design and contribute to reusable agentic components, libraries, and common services
Provide expert-level troubleshooting for complex architectural and integration challenges
Requirements
Bachelor's degree in Computer Science, Data Science, a related field, or equivalent practical experience
Nice to have: Healthcare payer domain knowledge
Nice to have: Solution Architect Associate certification (preferably in GCP or AWS)
Build and scale training libraries and infrastructure for autonomous driving models, enabling training on thousands of GPUs and massive datasets.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Researcher intern focused on LLM reliability and uncertainty for AI science assistants
Compensation
Annual Salary Range (based on full-time 40 hours per week) / Annual Salary Range: $44,500 - $71,200
Responsibilities
Research and evaluate uncertainty quantification and hallucination detection methods for multi-turn, agentic scientific workflows
Develop probing methods that predict, from a model's internal representations, when a scientific task specification is incomplete or inconsistent and a clarifying question is warranted
Build and instrument evaluation pipelines that capture and analyze model internal states over multi-turn scientific dialogue on HPC systems
Conduct experiments and analyze model behavior across computational science domains and established benchmarks
Contribute to technical documentation, research reports, publications, and presentations summarizing project progress and findings
Build and scale deep learning infrastructure libraries and frameworks for autonomous driving models.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Compensation starts at 190,000 CZK per month, with the final offer determined by the candidate’s seniority and experience.
Responsibilities
Own the technical vision and system architecture for machine learning workflows driving Depo Copilot, Depo Summaries, and the Meetings and Notetaking app.
Architect and build LLM pipelines capable of reasoning over extensive case files and real‑time transcripts, ensuring all outputs are fully grounded in source material.
Determine optimal model strategies – evaluating when custom fine‑tuning or trained models are required over prompt engineering – and lead implementation.
Establish evaluation frameworks alongside our legal experts and annotators, creating custom datasets, rubrics, offline metrics, and release checks to proactively prevent regressions.
Optimize pipeline efficiency, latency, and operational costs for live streaming analysis using strategic model selection, fallbacks, and precomputation.
Tech Lead, Machine Learning (Real-time AI)Filevine · Prague, Czech Republic+1 more