Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Design and implement scalable AI systems and data pipelines using Python and TensorFlow. Perform data wrangling, ML model development (including NLP and deep learning), integrate diverse data sources, analyze patterns, collaborate with stakeholders, and mentor junior team members to align AI initiatives with business goals.

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 design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

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, deploy, and maintain risk and eligibility models (credit, accident, fraud, behavioral). Manage modeling platform and vendor integrations, monitor model performance and drift, perform feature selection and validation, and collaborate with Risk, Finance, Product, and Engineering to set thresholds and translate results into actionable recommendations.

Design, develop, and deploy scalable ML software systems for healthcare. Collaborate with teams to improve products and ensure performance and reliability.