Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Perform data analysis and test & evaluation of machine learning algorithms; develop algorithms for physical and autonomous systems; transition algorithms to software and embedded systems; work with stakeholders to present results and solutions; research new ML and data-analysis methods; handle large, multidimensional datasets and data pipelines.

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.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

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 AI Engineer responsible for developing AI-powered products, optimizing large language model inference, and collaborating across teams to enhance customer interactions and business value.

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.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

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.

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.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.