Top AI & Machine Learning Jobs in Boston, MA

Drive applied research to build and deploy AI foundation models end-to-end. Partner with cross-functional teams to design, train, evaluate, validate, and implement large-scale models using open-source tools and cloud infrastructure. Mentor scientists, translate research into business goals, and publish/represent Capital One in the research community.

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 a cross-organizational program to transition engineering to an AI-driven, self-optimizing ecosystem with robust telemetry and evaluation. Orchestrate strategy and execution, mitigate technical risk, build and grow a TPM practice, align product/engineering/design/data science, and deliver measurable business impact.

As an Applied Researcher II, you'll develop AI models, collaborate across teams, conduct applied research, and drive innovations in AI to improve customer experiences.

The role involves leading AI solutions development, optimizing AI systems, and collaborating with cross-functional teams to enhance customer interactions at Capital One.

The Applied Researcher I role involves developing AI foundation models, collaborating with cross-functional teams, and conducting advanced research to enhance banking experiences with AI.

As a Lead AI Engineer, you will design, develop, and support AI software components while partnering with cross-functional teams to deliver AI-powered products at Capital One.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.

The Applied Researcher II will develop AI-powered products, partner with cross-functional teams, engage in applied research, and implement AI foundation models.

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.