Top Hybrid Machine Learning Engineer Jobs in Boston, MA

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 design, build, deploy, and maintain production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to create data pipelines, retrain and monitor models, and apply responsible, explainable AI practices while contributing technical vision and roadmap for AI solutions.

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.

Lead development and implementation of advanced AI/ML solutions, design and optimize models and algorithms, transform raw data into actionable insights, manage projects end-to-end, supervise and mentor teams, maintain quality and standards, and manage client engagements and expectations.

Lead development of predictive and prescriptive supply chain models using statistics, machine learning, and optimization. Partner with IT, product managers, and engineers to scale analytics products, design experiments, run simulations, mentor analysts, and translate insights into operational improvements.

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.

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.

Individual contributor on an applied science team building production ML and software systems for media accessibility. Responsibilities include prototyping, evaluation, productionization, experiment design and analysis, code and design reviews, and reporting results for audio/video/NLP/generative AI projects.

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.