Top Machine Learning Engineer Jobs in Boston, MA

Design, implement, and deploy ML models and scalable data pipelines for multimodal behavioral and clinical data. Clean, integrate, analyze, and visualize time-series and wearable data; build real-time dashboards/apps; evaluate model performance; supervise analyses; collaborate with IT; and support publications and grant preparation.

As a Senior Staff Data Scientist/Machine Learning Engineer, you will develop machine learning models to enhance Autodesk's eCommerce platforms, drive data-informed decisions, and mentor junior team members.

Lead the design and delivery of large-scale machine learning systems, mentoring engineers while driving technical strategy and collaboration across teams in AEC solutions.

As a Principal Machine Learning Engineer, you will develop distributed inference infrastructure, integrate with vLLM, and optimize inference workloads, mentoring fellow engineers along the way.

The Principal Machine Learning Engineer will develop optimization algorithms, enhance LLM deployments, and collaborate with research teams on model performance and compression.

The role involves designing, optimizing, and deploying machine learning models for edge environments, focusing on computer vision. The engineer will collaborate with teams to integrate models and improve inference efficiency while mentoring peers and upholding company values.

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

As a Machine Learning Engineer, you'll design and ship LLM-based solutions, collaborate across teams, and improve storytelling capabilities at scale.

As a Machine Learning Engineer, you will build and improve ML models for identity verification, focusing on data processing, model training, and deployment in real-world scenarios.

As a Cloud Machine Learning Engineer at Hugging Face, you will build ML solutions leveraging cloud technologies, integrate models, design APIs, and contribute to documentation and community advocacy.

Lead design and implementation of LLM observability features: prototype and scale product capabilities for tracing, evaluating, and debugging generative AI systems. Work cross-functionally to influence architecture, mentor engineers, prioritize customer pain points, and drive product and engineering decisions for reliable, high-performance AI observability.

Lead the design, development, and deployment of machine learning models, collaborating with teams and mentoring engineers. Focus on scalable ML workflows.

Design, build, and maintain ML and CV infrastructure to support the end-to-end ML lifecycle. Develop and operate production-grade microservices and containerized deployments on cloud (primarily AWS). Collaborate cross-functionally, contribute to CI/CD and infrastructure automation, mentor engineers, and ensure high engineering and testing standards.

Lead definition and implementation of the companys ML strategy and infrastructure. Build end-to-end production ML systems (data pipelines, training, evaluation, serving), own data strategy and feedback loops, integrate ML with product/backend, write production-quality code, and recruit/mentor the ML team as it scales.

The Principal Machine Learning Engineer will improve diffusion models, conduct research, implement techniques, and collaborate on production-ready systems in AI for fashion.

Lead the development of advanced perception algorithms for self-driving vehicles. Collaborate and mentor engineers, optimize algorithms, and implement sensor fusion for robust perception systems.

The Machine Learning Engineer will develop and integrate ML solutions, maintain ML systems, build data pipelines, and collaborate with cross-functional teams.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

The role involves building deep learning models for ad candidate retrieval and ranking, improving recommendation systems, and taking ownership of ML systems.

The Senior Software Engineer will lead technical areas, manage AI/ML projects, develop model services, APIs, and collaborate on design and UX to enhance cloud-based AI solutions.

Lead architecture and engineering for production-grade ML in healthcare: design MLOps CI/CD, scalable low-latency inference for LLMs and classical models, build reliable data pipelines for FHIR/HL7/DICOM, manage cloud IaC and observability, ensure HIPAA/HITRUST compliance, and mentor engineering teams to operationalize agentic AI workflows.

As a Principal AI/ML Engineer, you will design large-scale intelligent systems, integrate AI features, and lead technical strategies while mentoring other engineers.

The Machine Learning Engineer will develop ML models, manage data pipelines, and deploy applications improving healthcare processes using AI and NLP technologies.

Lead the design and deployment of machine learning models, collaborate across teams, mentor staff, and manage data science pipelines using cloud technologies.

As a Staff Machine Learning Engineer, you will optimize Quora's ad products by applying machine learning algorithms, taking ownership of systems, and collaborating across teams.