Machine Learning Jobs in Cambridge, MA

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

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

Lead data-quality and labeling efforts for perception ML: implement scalable quality measures, drive data-centric model improvements, maintain ontology/class structure, evaluate human vs auto-labeling trade-offs, and build metrics for labeling throughput and annotator/vendor quality.

As a Staff Machine Learning Engineer at Motional, you'll define ML systems' architecture, lead projects, mentor engineers, and drive technical strategy to optimize data discovery and model improvement for autonomous vehicles.

Design and implement scalable LLM- and RAG-based systems integrating knowledge graphs and multi-omics data, build agentic orchestration and conversational AI, collaborate with data engineering and experimental scientists, and deploy pipelines that synthesize biological information to support therapeutic discovery.

The AI/ML Informatics Intern will design, build, and evaluate an AI tool for health data analysis, involving data integration and user testing.

Lead the scientific direction and machine learning development for climate modeling. Manage model training, evaluation, and collaboration across teams to develop predictive systems based on hydrology and climate data.

As a Machine Learning Scientist, you'll design and implement frameworks for scientific reasoning using LLMs, collaborate with scientists, and create scalable solutions.

Develop and iterate machine learning models for complex datasets while collaborating closely with technical teams to shape long-term ML strategy.

Seeking a Machine Learning Engineer with 3 years of experience to develop and deploy ML systems, optimize models, and work collaboratively with teams to enhance ML capabilities.

Lead the design and implementation of ML systems, set standards, mentor teams in MLOps practices, and ensure the reliability of production ML models.

The Cloud and ML Infrastructure Engineer will maintain complex systems, manage AWS production infrastructure for ML/AI workloads, and design monitoring systems for algorithms.

Lead ML/AI-driven computational protein and antibody design efforts, advance de novo and engineering pipelines, collaborate with experimental teams to test and iterate constructs, implement state-of-the-art modeling tools, shape computational strategy, and manage external research partnerships.

The MLOps Engineer focuses on developing tools and processes for ML workflows, empowering engineers, and ensuring high-quality model deployment. Key responsibilities include managing distributed systems, improving observability, and advocating best practices across teams.

The role involves training and deploying deep learning models for semantic reasoning in autonomous vehicles, focusing on highway environments and optimizing systems for real-time performance.

The Vice President of Customer Success will lead strategies for customer onboarding, retention, and satisfaction while managing the Customer Success team and collaborating with executives to align customer experience with business goals.