Top Machine Learning Engineer Jobs in Boston, MA

The Machine Learning Engineer will design, implement, and manage complex ML algorithms, focusing on computer vision and data analysis while collaborating with team members to transition algorithms to production.

The role involves designing and optimizing large-scale multimodal AI models, conducting applied research, and collaborating with cross-functional teams to deliver impactful member value at WHOOP.

The role entails managing an Agile team focused on design, development, and implementation of scalable machine learning applications while ensuring architecture performance and model governance.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Design, build, and scale production ML systems (including LLMs) to automate customer support and compliance workflows. Lead technical roadmaps, write high-quality Python code, mentor engineers, ensure secure, scalable architectures, and resolve complex reliability issues.

The role involves deploying ML models for ads, collaborating with teams to enhance solutions, and mentoring junior scientists, requiring advanced technical and communication skills.

The role involves designing and operating AI systems, building data pipelines, fine-tuning models, and mentoring team members while collaborating with product and data science teams.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Lead and implement AI/ML platform solutions to enhance patient care by developing scalable systems, mentoring engineers, collaborating with cross-functional teams, and leveraging emerging technologies.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Build and deploy AI/ML models for various asset categories, design workflows, and quickly prototype solutions using customer data.

Design, develop, and deliver machine learning models for clinical data. Collaborate with stakeholders and optimize production ML systems.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The MLOps Engineer II designs and supports an ML platform, enhancing workflows and tooling for ML engineers, thus improving speed and quality of model deployment.

Lead a team of developers in creating cloud-based solutions using modern technologies, while mentoring peers and collaborating with product managers.

As a Senior Machine Learning Engineer in the Health team, you'll design and productionize ML systems for personalized health metrics, collaborating with data scientists and engineers to optimize performance and ensure service reliability.

The Senior Software Engineer will lead technical areas, drive projects, and build AI/ML features using Python and Go while ensuring user-friendly experiences.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The Senior Full-Stack Engineer II will lead technical areas, build and scale AI applications, develop APIs and collaborate with cross-functional teams.

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.

The Senior Lead Machine Learning Engineer will design, develop, and implement machine learning systems, collaborating with teams to solve business problems using machine learning applications.

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