Artificial Intelligence Jobs in Cambridge, MA

Lead development and implementation of the firm's attorney AI learning strategy. Assess training needs, design curricula, credentialing, and workshops; coordinate cross-functionally with KM, IT, and practice leaders; track participation and outcomes; monitor legal AI tools and translate developments into practical lawyer-facing training and guidance.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

Lead and contribute to the end-to-end design, development, and operation of scalable AI-enabled data products for Oncology R&D. Integrate diverse data sources into AI-ready assets, design data models and pipelines, partner with stakeholders to deliver business-critical use cases, leverage cloud platforms and tools (Python, SQL, AWS), implement quality and KPIs, mentor team members, and drive Agile and reuse-focused practices to support clinical, pre-clinical, RWD and omics projects.

Design, build, and maintain scalable, secure data pipelines and platforms to acquire, transform, and store large financial datasets. Enable AI/GenAI-ready data foundations for model training, inference, retrieval, and RAG workflows. Collaborate with cross-functional teams, ensure data quality, governance, observability, and compliance, and support production deployments and troubleshooting.

Own and build the documentation-as-system: docs-as-code repo, CI/CD publishing pipelines, AI-augmented authoring and validation workflows, and agent-consumable content. Generate reference docs from API specs, run SME review and release notes cadence, maintain style guide and runbooks, and enable internal contributors to ship docs through automation and tooling.

Lead cross-functional teams to design and deliver scalable machine learning solutions for drug discovery. Develop core ML capabilities, advanced algorithms, and large-scale model training workflows; translate model outputs into scientific hypotheses; manage academic collaborations; define success metrics and promote adoption of advanced AI methods across research.

Develop scalable cloud infrastructure and robust software solutions for AI/ML, collaborating with engineers and scientists to enhance scientific research.

Remote participants who are native or expert-level Italian speakers will use a qualifying Google Pixel device for 14 consecutive days to interact with notification-generating apps, complete daily notification-sorting tasks, and submit short daily feedback. Participants must follow confidentiality terms and complete background screening. Project pays $250 upon successful completion.

Lead firmwide AI enablement by setting strategy and roadmap, designing multi-level training, running an AI champion community, producing adoption analytics, shaping governance, evaluating tools and vendors, and managing stakeholders and a small team to drive responsible AI adoption and measurable business impact.

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.

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 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.

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.

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.

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

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.

As an AI Lead, you will deploy models on prototype hardware, develop algorithms, work with hardware engineers to refine architecture, and support market strategy development.