Design and develop advanced AI algorithms for next-generation video analytics, leveraging modern AI models and architectures to solve complex video understanding challenges.
Responsibilities
Research and explore state-of-the-art AI models, Computer Vision techniques, and emerging technologies to identify new opportunities for advancing video analytics capabilities.
Develop algorithms and solutions for challenging video analytics and video understanding problems, leveraging Computer Vision, deep learning, LLMs, VLMs, and other relevant AI technologies.
Experiment and evaluate different models, algorithms, and approaches, build proof-of-concepts, and assess their performance and suitability for real-world applications.
Integrate algorithms and AI models into existing software systems, collaborating with software engineers to deliver complete product capabilities.
Optimize algorithms and model inference for performance, accuracy, and resource utilization, meeting production requirements and hardware constraints.
Requirements
Master's degree in Computer Science, mathematics, EE or a related field
Lead AI engineering teams at Blue Cross Blue Shield of Massachusetts, driving technical excellence and building production‑grade AI solutions for 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.
Responsibilities
Provide technical leadership and mentorship to AI engineers
Lead solution design and implementation of agentic AI systems and generative AI integrations
Collaborate with product owners to refine requirements and deliver features
Build scalable AI systems and manage performance, reliability, and observability
Troubleshoot complex ML and cloud infrastructure issues
Requirements
Bachelor's degree in Computer Science, Engineering, Technology, or a related field
AI Solution Architect
Blue Cross Blue Shield Of Massachusetts · Posted Dec 19, 2025
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.
Research and evaluate uncertainty quantification and hallucination detection methods for multi-turn, agentic scientific workflows; develop probing methods; build evaluation pipelines; conduct experiments; contribute to documentation and publications; develop, test, and maintain research code.
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.
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