Machine Learning Jobs in Cambridge, MA

The Clinical Development Scientist, Director will lead complex clinical trials in Inflammation and Immunology, ensuring scientific quality and patient safety. Responsibilities include protocol development, data interpretation, and implementing AI solutions in clinical workflows.

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Manage AI engineering teams and client relationships, drive project deliverables, mentor staff, and uphold quality standards while leveraging advanced technologies.

Lead product strategy and roadmap for Klaviyo's channel infrastructure and mobile ecosystems (iOS, Android, SDKs). Manage cross-functional teams, own end-to-end product lifecycle, drive scalability and performance for email, push, in-app, and other messaging channels, mentor product managers, and partner with platform stakeholders to deliver enterprise-grade solutions.

Lead the Customer Agent AI project at Klaviyo, managing engineering teams, defining technical direction, and driving architectural decisions to develop a conversational AI platform.

Lead global fraud risk strategy for payments: design frameworks, oversee detection (merchant fraud, ATO, social engineering), build data-driven real-time decisioning, run incident response and war rooms, advise product/engineering on embedded fraud controls, and guide expansion into new geographies.

The role involves driving business development in biotech and biopharma, managing strategic accounts, and collaborating with cross-functional teams to close multi-million-dollar enterprise deals.

The role involves leading AI strategy and implementation, overseeing teams, managing client accounts, and delivering strategic business solutions to enhance client value.

The Senior Security Engineer will build and maintain detection systems, automate responses, and lead incident investigations to enhance security observability.

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

The role involves designing and implementing advanced analytics solutions for healthcare payers, analyzing complex problems, mentoring juniors, and building client relationships.

The Technical Lead for ML Operations at WHOOP will drive the strategy and execution of ML infrastructure, lead a small team, design scalable systems, and collaborate with multiple stakeholders to enhance the ML platform.

Develop and scale machine learning systems for physiological signal processing. Design and train models, ensure compliance, and mentor team members while collaborating with cross-functional teams.

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 finance transformation engagements using Oracle Cloud ERP and EPM. Design and implement Oracle Financials and Hyperion solutions, integrate RPA/ML/analytics, ensure compliance, manage stakeholder relationships, coach teams, and drive strategic outcomes on large, cross-border projects.

Lead an Agile team to design, build, deploy, and maintain production-scale ML models and pipelines. Drive ML architecture, model training, CI/CD, monitoring, and cloud/Kubernetes-based deployments while ensuring responsible, explainable, and well-governed ML solutions.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

Develop and deploy scalable ML systems for health metrics from physiological data. Collaborate with data scientists, MLOps, and engineers to productionize models, improve data pipelines, ensure reliability, and support on-call operations to maintain uptime and performance.

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, build, and productionize scalable ML systems for health features using physiological time-series data. Collaborate with data scientists, engineers, and product teams to translate prototypes into reliable, cost-efficient production services, improve pipelines and tooling, and participate in on-call rotations for data science services.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

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.

This role focuses on conducting research in equity markets using ML and NLP for alpha generation. You'll collaborate with global teams to develop models and translate innovations into strategies.