Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Perform Bayesian and probabilistic modeling, test and evaluate ML/AI systems, analyze complex datasets, develop algorithms, and transition solutions into software and real-time embedded systems for national security customers. Communicate results and propose technical paths forward.

Design, build, test, and deploy machine learning models and systems; research ML methods; implement scalable ML applications; collaborate with engineers, product managers, and data scientists to deliver production-ready analytics solutions.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

Build and ship full‑stack web features and microservices for a cloud‑native genomics platform. Lead design and code reviews, mentor engineers, and develop production LLM applications (RAG, evaluation, citations) using managed endpoints and containerized CI/CD deployments. Collaborate with Product, QA, and Architecture to deliver reliable, regulated healthcare software.

Lead end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

Lead development and deployment of predictive and prescriptive models focused on supply chain and causal modeling. Partner cross-functionally to translate business problems into data science solutions, mentor team members, explore large datasets, communicate results to senior stakeholders, and contribute to the ML/AI strategy.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Lead large, cross-functional technical programs for autonomy, owning model development and release cycles. Act as DRI for behavioral capabilities, integrate Prediction and ML Planning, dig into ML/behavior issues, identify and mitigate technical risks, and align roadmaps with engineering, product, and executive stakeholders to deliver driverless milestones.