Top Hybrid Machine Learning Engineer Jobs in Boston, MA

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.

As a Senior Lead Machine Learning Engineer, you'll design and implement machine learning applications, lead teams, and optimize systems and data pipelines while collaborating in an Agile environment.

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.

Implement and productionize ML research into performant C++ and Python systems, build ML infrastructure and training pipelines, design LLM/agent applications, and deploy AI workloads across edge, near-edge, and cloud environments while collaborating with data science and engineering teams.

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.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.