Top Machine Learning Jobs in Boston, MA

You will train, evaluate, and deploy ML models for scene understanding and behavior prediction in autonomous vehicles, while collaborating with engineers.

Responsible for training and deploying ML models for scene understanding, behavior prediction, and planning in autonomous vehicles. Collaborate on designing and analyzing experiments, maintaining evaluation code, and improving model performance.

As a Principal Engineer on the Behaviors team, you will guide the technology direction, lead complex initiatives, and mentor engineers to enhance autonomous vehicle capabilities through ML solutions.

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 Senior Machine Learning Engineer will design, prototype, build AI systems, evaluate ML models, and improve Vectara's AI Agents with a focus on reliability and performance.

The role involves building AI technologies for customer service, developing ML systems, and collaborating with cross-functional teams to enhance user experiences.

As a Senior Machine Learning Engineer on the Perception Team, you will define technology direction, architect solutions for autonomous vehicles, collaborate with team members, and mentor junior engineers, contributing to high-stakes projects in autonomous driving.

The ML Engineer will work on RL frameworks, develop synthetic data engines, and maintain performance evaluations for a closed-loop drug discovery platform.

The role involves building a recommendation engine, designing ML systems, making data-driven decisions, and collaborating with leadership to enhance user experiences.

Lead cross-functional ML/AI initiatives, drive technical roadmaps, mentor teams, and deliver impactful ML/AI solutions for business growth.

Develop and optimize machine learning models and workflows to enhance insurance processes. Collaborate on model integration, monitoring, and improvement while driving innovation in modeling techniques.

Research Engineer will conduct Machine Learning research to enhance localization and global entertainment, focusing on ASR, Speaker Diarization, and Audio Understanding.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

Lead the ML and AI engineering team, strategizing and implementing AI solutions for business growth, mentoring staff, and collaborating across departments.

The intern will conduct research on machine learning and driving behaviors, work with driving datasets, collaborate on deploying results, and publish findings.

Build and optimize machine learning models for 3D dental applications, research state-of-the-art methods, and implement MLOps best practices.

Lead the design, execution, and analysis of machine learning-driven product experiments to optimize ordering systems. Collaborate with stakeholders and mentor teams.

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

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

As a Machine Learning Engineer, you'll evaluate neural network quality, coordinate with researchers, manage experiments, and report results to streamline AI software development.

Design and develop geometry processing algorithms and simulation models, manage datasets, and enhance machine learning workflows for engineering applications.

Develop and apply ML methods to create psychological tools, collaborating with experts, building data pipelines, and integrating systems into real-world applications.

Design and build ML features using LLMs, collaborate on technical architecture, and ensure safety and performance of deployments.