Machine Learning Jobs in Cambridge, MA

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.

Lead analytics delivery and develop data-driven initiatives using SQL, R, and Python. Build dashboards (Tableau, Power BI, Qlik), query Snowflake/Athena, apply NLP and ML (LDA, DBSCAN, BERT) to extract insights, support automation, monitoring, incident response, and collaborate with cross-functional Agile teams to optimize customer experience and operational KPIs.

Lead and coordinate external technology audit activities, perform technology risk assessments, develop control strategies for cloud, DevOps, and emerging technologies (AI/ML/Snowflake), document and evaluate ITGC/ITAC controls, track findings, and support audit readiness and remediation efforts.

Own end-to-end product design for an LLM-powered healthcare platform. Design and prototype intuitive, accessible interfaces for clinical users, collaborate with engineering, product, and clinical teams, and shape design culture and processes as an early-design hire.

The Director, Data Science oversees data-driven solutions, manages the Data Science team, and collaborates with business units and product development to implement advanced techniques. Provides strategic leadership and mentorship to enhance team effectiveness.

Manage the Autonomy Platform, overseeing roadmap and simulation for ML and robotics. Collaborate with internal teams to ensure reliability and performance based on customer needs.

As a Functional Consultant, you will drive consulting for clients in investment banking, analyze FO systems, guide business strategies, and create documentation for transformations and migrations.

The role involves leading product strategy and execution, managing product backlogs, and collaborating with cross-functional teams to deliver AI-driven solutions.

Seeking a Senior AI Engineer to develop AI solutions, oversee project delivery, and collaborate on system architecture. Requires 5+ years with Python and AI technologies.

Implement and maintain record-matching and text-analytics components, translate linguistic theory into production features, optimize search internals (Elasticsearch), collaborate in agile sprints, and help design scalable APIs and architectures to improve record-matching quality and reliability.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

Lead the end-to-end mobile panelist experience and coordinate device, firmware, and backend product roadmaps. Define success metrics, manage releases and agile processes, and partner with engineering, analytics, and operations to deliver scalable passive measurement and high data quality.

The Director & Group Head of Applied AI will lead AI strategy in drug discovery, collaborating with teams to deploy machine learning solutions and drive innovation in biomedical research.

As an MLOps Engineer at Kensho, you will develop tools and processes for ML workflows, assist ML engineers, and drive adoption of best practices across teams.

Design, implement, test, deploy, and iterate machine learning and optimization models and systems. Collaborate with data scientists, engineers, and product managers to scale ML solutions, research new techniques, and improve data quality and predictive applications.

Lead the Machine Learning Operations team to develop a robust ML platform, manage projects, and enhance AI tooling integration while driving engineering excellence.

Lead the development of AI applications and data products to enhance Immunology R&D. Manage a technical team and collaborate across departments.

The Head of Investor Relations will build and lead the IR function for a newly public biotech company, ensuring clear communication between the company and investors, drafting materials, and deepening scientific understanding.

The Enterprise Account Lead will drive revenue and manage client relationships in physical sciences, blending technical and business acumen to secure enterprise contracts and inform product strategy.

Lead product strategy and delivery for AI-driven clinical design and biostatistics products. Define roadmaps, guide statistical and ML approaches, ensure validation and compliance, partner with cross-functional and executive stakeholders, build external partnerships, and drive adoption to reduce development cycle-time, risk, and costs.

The Senior Manager of AI Inference at Red Hat leads engineering teams to innovate in AI and ML, focusing on model performance and MLOps software development. Responsibilities include setting vision, mentoring team members, and engaging in open-source communities.

Lead strategy, roadmap, and execution for consumer search growth across web, mobile, and email. Define metrics, design A/B tests, drive product lifecycle from ideation to launch, and collaborate cross-functionally to improve acquisition, engagement, retention, and conversion.

Develop and validate machine learning and data analysis solutions for underwriting. Clean and analyze large datasets, design experiments, build and optimize models, collaborate with engineers and cross-functional partners, and communicate findings to technical and non-technical stakeholders.

Lead development, deployment, and maintenance of production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to build data pipelines, fine-tune foundation models, apply responsible AI practices, and contribute technical vision for scalable, high-performance AI infrastructure in risk management.

Build, deploy, and maintain production ML and AI systems (including LLMs, RAG, similarity search, guardrails, observability). Partner with cross-functional teams to design data pipelines, fine-tune models, monitor performance, and apply Responsible/Explainable AI best practices while contributing technical vision and roadmap for risk-focused AI products.