Artificial Intelligence Jobs in Cambridge, MA

The Marketing Systems & AI Automation Manager will create automated marketing workflows, integrate systems, optimize data management, and enhance GTM strategies using advanced technologies and tools.

Define and own the AI product roadmap, prioritise high-value opportunities, lead cross-functional delivery from concept to production, set success metrics and governance, manage risk and evaluation, and ensure AI programmes deliver measurable business outcomes.

The Director of CX AI will develop and execute the AI roadmap for customer experience, evaluate tools, modernize workflows, and ensure responsible AI use.

Design and build intelligent agent-driven systems for AI-accelerated process engineering. Work on developing methods for AI agents to optimize complex processes.

The Infrastructure Operations Lead manages cloud operations, oversees AI and GenAI implementations, and drives optimization and compliance efforts within a healthcare environment.

The Agentic AI Engineer will design and build customized AI agent solutions tailored to client needs, manage full delivery, and ensure production quality.

The Principal TRI AI Governance Specialist manages DTCC's AI governance framework, ensuring compliance and risk management while promoting responsible AI use across the organization.

The role involves leading evaluations of utility energy programs, developing methodologies, driving business development, mentoring teams, and engaging clients effectively.

Build agentic applications and tooling with LLM infrastructure, requiring hands-on engineering in a high-pressure environment. Deliver quality under tight deadlines.

The Product Manager will lead the strategy, development, and integration of AI solutions, enhancing research processes and collaboration across teams.

The Senior Software Engineer will build, release, and manage the Red Hat AI Inference Server, focusing on CI/CD pipelines and multi-cloud GPU infrastructure.

Lead the Accounting Strategy and Financial Reporting team, focusing on M&A, investments, and new accounting standards while ensuring compliance with US GAAP and SEC requirements.

Lead design, training, validation, and production deployment of advanced ML models (deep learning and graph-based) for drug discovery. Build scalable inference services and automated MLOps pipelines, optimize performance and cost, implement monitoring and drift detection, benchmark models against scientific objectives, collaborate with scientists, and mentor engineering teams to ensure reproducible, production-ready AI solutions.

Develop tactical insights from large structured and unstructured datasets, build and validate ML models and AI tools, design scalable resilient distributed systems, collaborate with analytics and data teams, communicate results to technical and executive stakeholders, and influence departmental strategy while independently driving technical approaches.

Lead the strategic roadmap for Clinical AI in Translational Research, integrating scientific needs with data science methodologies for decision-making and implementation.

Design, build, and scale AI-powered internal tools, agents, and automations to replace manual processes across HR, Finance, Operations, and Legal. Prototype and productionize solutions using ChatGPT, Copilot, APIs, and structured data, create reusable prompts/templates, measure adoption, and train colleagues to drive enterprise AI usage and productivity.

Build AI-native offensive security capabilities: develop AI-assisted vulnerability discovery, exploit validation, reverse-engineering and pen-testing pipelines; design agentic workflows and eval harnesses; perform static/dynamic analysis and authorized live testing; produce reproducible technical reports and improve safety, auditability, and human-in-the-loop controls.

Design and build a scalable agentic AI framework integrating MCP/A2A, LLMs, and orchestration with enterprise data pipelines. Implement APIs/SDKs and tooling, ensure secure multi-tenant production deployments on Kubernetes/Docker, evaluate new agentic/LLM technologies, and mentor engineers to establish AI engineering best practices.

Build and own full-stack AI platform systems: backend services, data pipelines, frontends, and cloud infrastructure. Integrate LLM agents with lab data, instruments, and databases; prototype and harden agentic workflows; write well-tested code; mentor teammates and drive engineering best practices in a research-focused environment.

The AI Scientist will lead the development of biomolecular modeling technologies, focusing on creating and validating AI models for drug design using experimental feedback.

Lead evaluation, piloting, and rollout of AI-enabled tools and automated workflows for legal teams. Collaborate with attorneys, practice leaders, and tech partners to design solutions, drive adoption, deliver training, and measure impact to improve legal service delivery and internal operations.

The Applied AI/Bio Scientist will utilize AI to design biologics, collaborate with teams, and improve discovery processes in therapeutic design.

The Senior AI Risk Advisor will lead AI risk assessments, develop governance frameworks, evaluate AI vendors, and engage with stakeholders on AI adoption.

The AI Platform Engineer will develop full-stack systems connecting AI models to real-world applications in drug design, collaborating with scientists and ensuring rapid experimentation cycles.

The AI and Digital Innovation intern will support AI initiative coordination, experimentation, staff training, and documentation of responsible AI practices within Oxfam.