Artificial Intelligence Jobs in Cambridge, MA

Partner with Field and Product teams to design and implement LLM observability architectures, build proofs-of-concept, produce technical collateral, and advise customers to drive adoption and product feedback.

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 ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Lead enterprise-level strategy and delivery for building design, engineering, data readiness, and AI adoption. Define customer success plans, run workshops, shape consulting offerings, mentor staff, drive change management, and ensure high-quality project delivery and measurable customer value.

The Senior Solution Architect leads the architectural strategy for finance systems, aligning technology with business goals, ensuring compliance, and driving innovation through AI and ML integration.

As a Solution Consultant, you will help Life Sciences companies implement Veeva's Vault Safety platform, driving innovation and streamlining pharmacovigilance processes.

As a Solution Consultant, you will bridge pharmacovigilance challenges with Veeva's solutions, showcase value, build relationships, and innovate processes in the Life Sciences industry.

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.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

Lead the development of AI software components for Capital One, including model training, inference, and system optimization. Collaborate with cross-functional teams to deliver groundbreaking AI solutions, ensuring quality and scalability while mentoring junior team members.

The role involves developing AI foundation models, collaborating with cross-functional teams, and conducting applied research to enhance customer experiences using advanced AI techniques.

As a Lead AI Engineer at Capital One, you will design and develop AI systems, collaborate with cross-functional teams, and optimize AI solutions for performance and scalability.

Lead design, development, deployment, and optimization of large-scale AI/ML systems (training, inference, orchestration, observability). Collaborate cross-functionally to build responsible, scalable generative AI and agentic platforms, improve performance and cost, and shape long-term ML systems roadmap.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Build and operate high-performance LLM and multimodal model serving infrastructure using vLLM, SGLang, Kubernetes, and distributed GPU systems. Responsibilities include optimizing parallelism and inference configurations, benchmarking latency and throughput, analyzing model-serving architecture, profiling bottlenecks, and deploying new models to production. The role collaborates with hardware and systems engineers to define backend architecture and service requirements for long-context, agentic, RAG, and coding workloads.

Designs and enhances an enterprise data and AI platform, building reusable AI starter kits, reference implementations, templates, and developer-focused patterns. Partners with engineering, governance, and product teams to embed governance, accelerate AI adoption, and reduce delivery time. Leads enablement programs, champions cloud-first innovation, advises cross-functional initiatives, and mentors teams developing and deploying AI solutions. Requires expertise in AI/ML, GenAI, cloud data products, Snowflake or Databricks, agile delivery, and enterprise software development.

Build and own CloudZero’s governed data and AI platform, including Snowflake pipelines, modeled data products, data quality, retrieval systems, agent environments, identity inheritance, and cloud infrastructure. Automate Okta, Jamf, Google Workspace, Slack, Jira, and employee lifecycle workflows. Establish secure AI landing zones across AWS, GCP, Azure, and Snowflake, enable self-service agent development, and manage cost attribution, governance, and production reliability.

Drive go-to-market strategy and adoption for Kensho AI products by bridging technical AI capabilities and commercial teams. Lead customer demos, technical deep-dives, co-development partnerships, market analysis, and sales enablement while feeding product and strategy teams with market insights to expand revenue and partnerships.

Lead and grow an AI Platform engineering team delivering cloud-native infrastructure for AI/ML development. Own people leadership, hiring, roadmap delivery, architecture reviews, engineering standards, stakeholder communication, and team wellbeing while partnering across AI, product, and cloud teams.

Manage biographical data integrity for AA&D CRM (Advance): oversee scheduled data projects (graduation conversions, NCOA, voter files), design batch imports/exports, run data cleaning and QA, build queries/reports with SQL and BI partners, test changes, supervise one direct report, and collaborate across teams to improve processes and leverage AI/RPA for efficiency and accuracy.

The role involves teaching, strategic leadership, and administrative oversight for the MS in Health Informatics Program, ensuring curriculum relevance and program success across multiple campuses.

The Principal Research Scientist will lead research on resilient AI, manage projects, publish findings, secure grants, and supervise staff and students.

Provides specialized helpdesk support for enterprise AI tools, troubleshooting access, performance, integrations, and outages. Maintains issue documentation, monitors system readiness, validates configurations, supports AI training and tool rollouts, captures user feedback, and contributes to adoption reporting. The role also helps maintain AI governance, security, data management, and support procedures while recommending improvements to user experience and system effectiveness.

Lead design, development, deployment, and maintenance of scalable AI/ML tools and reusable pipelines to accelerate target and biomarker discovery. Collaborate with computational biologists, data scientists, and engineers to scope data and build solutions, provide tactical and strategic ML engineering support, drive SDLC best practices for Python packages and Docker images, and propose innovative AI/ML approaches aligned with industry standards.

Sets the technical vision for molecular AI and privacy-preserving federated learning within Lilly TuneLab. Leads research strategy, mentors scientists and engineers, and develops federated foundation models, multi-task predictors, generative chemistry systems, and ADMET-driven optimization pipelines. Oversees molecular property prediction, synthetic feasibility, validation, interpretability, benchmarking, and scientific dissemination across internal teams and biotech partners. The role requires deep expertise in machine learning, computational chemistry, drug discovery, federated optimization, and molecular design.