Artificial Intelligence Jobs in Cambridge, MA

Lead end-to-end product design for AI-powered experiences across web and mobile. Simplify complex workflows for frontline users, translate ambiguous AI concepts into testable solutions, contribute to scalable design systems, present design rationale, incorporate feedback, and partner closely with product and engineering teams. The role requires strong visual and interaction design, systems thinking, storytelling, and experience designing AI features such as natural language interfaces, agents, and predictive insights for B2B SaaS or enterprise products.

Own a large scaled-customer book, managing onboarding, adoption, renewals, retention, account health, and risk. Design and operate one-to-many customer education programs, including curricula, learning paths, webinars, lifecycle communications, digital onboarding, and self-service resources. Measure program impact through retention, engagement, activation, and re-engagement metrics. Partner with Support, Product, Sales, and Marketing to resolve blockers, identify expansion opportunities, and scale successful customer motions.

Lead the design, validation, deployment, governance, and continuous improvement of machine learning and predictive analytics solutions for U.S. Commercial operations. Analyze claims, EHR, and commercial data to support patient identification, treatment adherence, provider engagement, and commercial strategy. Partner with Commercial, Medical Affairs, and analytics teams, communicate insights to executives, mentor data science professionals, and manage vendors and cross-functional initiatives.

Develop and maintain web applications using React Native and JavaScript, integrate AI models and LLMs, collaborate with designers and product managers, write clean code, participate in code reviews, and stay current with emerging technologies.

As a Senior Lead AI Engineer, you will design and develop advanced AI systems, collaborate with cross-functional teams, and introduce innovative AI techniques while mentoring others.

The Lead AI Engineer will develop AI solutions, partner with cross-functional teams, and optimize large-scale production AI systems using various technologies.

The Senior Lead AI Engineer will collaborate with cross-functional teams to design, develop, and implement AI software solutions, focusing on large-scale production systems and AI model optimization.

As a Lead AI Engineer, you will develop, deploy, and support AI software components, collaborate with cross-functional teams, and optimize performance of large-scale AI systems.

Lead AI Engineer to design, build, deploy, and support production-scale vision and foundation-model components (training, inference, vector search, guardrails, evaluation, observability). Partner with cross-functional teams, optimize LLM training/inference for cost/latency/throughput, and help set the technical vision and roadmap for foundational AI systems at Capital One.

The Lead AI Engineer will design, develop, deploy, and support AI software components, collaborating with cross-functional teams to create scalable AI solutions that enhance customer interactions and operational efficiency.

The Lead AI Engineer will develop and deploy AI software components, optimize AI systems, and collaborate with cross-functional teams at Capital One to enhance customer interactions through AI technology.

Design, develop, deploy, and support foundational AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize training/inference for scalability, cost, latency, and throughput. Partner cross-functionally to deliver production AI services and shape the technical vision and roadmap for foundational AI at scale.

Lead design, develop, test, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search/VectorDBs, guardrails, evaluation, observability, and optimization for scalability, cost, latency, and throughput. Partner cross-functionally and contribute to technical vision and roadmap.

The Pre-Sales Data and AI Architect will design and operationalize enterprise data architectures, collaborating with teams to ensure effective integration of Salesforce D360 into complex ecosystems while driving customer success through innovative data solutions.

Build and teach reliable AI agent systems using LangChain, LangGraph, Deep Agents, and LangSmith. Design hands-on workshops, tutorials, reference implementations, and technical training. Provide customer guidance, create internal agents and tools, communicate customer feedback to Product and Engineering, and stay current with agent engineering practices. The role requires strong Python skills, live debugging and presentation abilities, customer-facing experience, and up to 20% travel.

Manages the operational execution of AI and biomedical research initiatives, including sponsor reporting, technical documentation, partner coordination, multi-site project tracking, agreements, onboarding, workshops, lab meetings, computing resources, software releases, datasets, and project websites. The role requires strong technical AI or computational science knowledge, exceptional writing and communication skills, research proposal experience, project management expertise, and familiarity with academic research environments.

Leads the integrated program for a Physical AI team, coordinating hardware builds, lab readiness, validation, engineering milestones, dependencies, risks, and cross-team decisions. Establishes planning cadences, dashboards, reporting processes, RACI frameworks, and program-management tools. Translates technical status into leadership updates, drives mitigation plans and accountability, facilitates hardware-software tradeoffs, and manages scope changes across engineering, operations, supply chain, facilities, IT, and external partners.

Build and deploy AI-powered tools and workflows for legal operations, including contract review, research, drafting, compliance, and knowledge management. Develop prompt frameworks, integrations, governance controls, quality procedures, and responsible-use policies. Evaluate vendors, assess technical and security risks, train legal teams, document workflows, track adoption and ROI, and collaborate with attorneys, IT, privacy, security, and business stakeholders.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Advise C-level life sciences and healthcare executives on AI and data strategies, lead large-scale digital transformations, develop scalable solutions, oversee high-quality consulting delivery, build long-term client relationships, generate new business, and manage cross-functional engagement teams and multimillion-dollar client portfolios.

The AI Solutions Director manages AI POD teams, leading complex customer pursuits, ensuring pipeline growth, revenue attainment, and mentoring team members in consultative selling.

Develop and maintain high-quality technical manuals, guides, specifications, and related documentation. Lead information sessions with subject matter experts, align content with user needs and product requirements, improve existing materials, manage documentation projects, incorporate usability-testing feedback, and coordinate with design teams on visuals and diagrams. Apply documentation tools, industry standards, and best practices to improve clarity, usability, and content quality.

Owns end-to-end architecture for a major generative AI platform and shared AI services. Responsibilities include designing model integrations, APIs, data flows, orchestration, RAG and agentic workflows; defining architecture standards, integration contracts, and non-functional requirements; ensuring security, resilience, compliance, governance, observability, and scalability; and conducting architecture reviews and proof-of-concept assessments. The role partners with product, engineering, data science, cloud, security, and control teams to deliver production-ready enterprise AI systems.

Leads AI enablement, advanced analytics, automation, process mining, and digital transformation across Global Clinical Operations. Develops analytics assets, low-code/no-code solutions, workflow automations, reusable AI frameworks, and platform capabilities. Evaluates emerging technologies, defines business requirements, supports proofs of concept, promotes responsible AI, and drives adoption through communities of practice, super-user networks, coaching, and stakeholder engagement. Partners with clinical operations, enterprise technology teams, vendors, and global matrixed organizations to improve clinical trial execution and operational performance.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, and evaluation. Collaborate with data and infra teams, drive architecture and inference decisions, and provide technical leadership and mentorship.