Top AI Jobs in Boston, MA

Lead program design and delivery for DTC data and AI initiatives: manage Snowflake-based data pipelines, build and deploy AI tools, design processes, ensure measurement governance (GA4), and align stakeholders to drive commercial growth.

Design and build agentic AI features by integrating LLM APIs into product workflows. Write well-tested TypeScript across the stack, implement tool invocation and multi-step reasoning flows, collaborate with Product and Design, and improve reliability and performance of agentic systems while documenting findings.

Lead GTM strategy for AI products, drive sales implementation and adoption, define KPIs/OKRs, enable teams with agentic AI workflows, and translate technical capabilities into measurable business outcomes across cross-functional stakeholders.

Partner with teams to deliver AI-powered products, build foundation models, and conduct applied research to enhance customer experiences.

Lead design, build, deploy, and support scalable, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Collaborate with cross-functional teams, optimize model performance and cost, and shape technical vision and roadmap for foundational AI platform services.

Lead design and build of scalable full‑stack AI enablement platforms and tools to accelerate engineering workflows. Champion experimentation, automation (code generation, testing, deployment), best practices, and cross‑team collaboration while mentoring engineers and delivering production‑ready solutions.

Lead an AI engineering team to design, build, deploy, and support foundation-model and LLM-based systems. Oversee model training, inference, similarity search, guardrails, evaluation, observability, and scalability optimizations. Make build-vs-buy decisions across OSS and SaaS AI technologies, set technical vision and roadmap, and recruit, mentor, and retain engineering talent.

Lead AI Test & Evaluation programs that measure mission-level outcomes across models, subsystems, and user workflows. Architect T&E systems, advise government customers, translate findings into engineering recommendations, run program execution (schedule/budget/team), and grow business opportunities while leading a multidisciplinary technical team.

Lead design, roadmap, procurement, and scaling of Mastercard's on‑premise AI infrastructure. Architect compute, GPU, storage, and networking for training and low‑latency inference at petabyte scale; run RFIs/RFPs; define data center and cooling requirements; partner with data science, MLOps, security, and governance; influence executives and evaluate emerging AI infrastructure technologies.

Lead end-to-end delivery of custom technology solutions across supply chain functions (planning, manufacturing, warehouse, logistics, merchandising). Define architectures and roadmaps, identify agentic AI use cases, guide development/testing/deployment, lead cross-functional teams, manage scope/timelines/budgets, and run client workshops to translate technical solutions into business outcomes.

Design and run AI-first onboarding, enablement, and learning programs for Klaviyo engineering. Partner with Dev Infra, L&D, Recruiting, and Eng Leadership to operationalize AI in workflows, codify engineering practices, run experiments, measure impact, and scale training to improve developer velocity, quality, and satisfaction.

Serve as a global SME for Industrial Metaverse technologies (PLCs, IoT, cloud/edge, digital twins, AI, EMC/RED, cybersecurity). Lead standards work, develop test programs, perform and supervise complex testing, train staff, represent UL externally, and support clients and continuous improvement across regional or global labs.

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

Lead design, development, deployment, and support of foundation model training and LLM inference platforms. Build similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, and scalability; contribute to technical vision and roadmap while partnering across engineering, research, product, and program teams.

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.

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 Technology Consultant will deliver technology solutions for Supply Chain and Manufacturing, focusing on data strategies and digital transformation across enterprises. This role involves leading projects with modern technologies and mentoring teams for continuous professional development.

Lead commercialization and GTM for Mastercard's U.S. Mid-Market Accelerator partner-led solutions. Define regional strategy, identify and structure partners, secure approvals, drive cross-functional execution, manage contracts and Salesforce pipeline, monitor post-launch performance, and enable internal teams and issuers to accelerate adoption and revenue growth.

Lead and build an Enterprise AI organization to define strategy, platforms, governance, and standards. Deliver scalable AI platforms (LLM gateways, model registries, vector DBs), enable business adoption, embed responsible AI and compliance, partner across Security, Legal, Finance, and drive measurable AI use cases and ROI while managing a small, cross-functional AI architecture and engineering team.

Lead US TMT business development with an AI focus: identify revenue opportunities from AI regulation, M&A, governance and data trends; develop client strategies, communications and targeted outreach; support pitches and major US TMT projects; coach and manage MBD colleagues; collaborate with partners and international teams to drive client relationships and revenue growth.

Lead policy strategy on blockchain, digital assets, and emerging technologies; advise cross-functional teams on regulatory, risk, and policy implications; engage regulators and industry groups; translate complex technical concepts for senior leaders and stakeholders to inform decision-making.

Lead and grow an AI engineering team to build internal copilots, agents, and an AI platform. Act as a player-coach contributing to architecture, prototypes, and code. Define technical roadmap, partner with business leaders to ship AI-native workflows, and ensure governance, observability, evaluation, and cost controls for LLM-driven systems.

Lead and execute SharkNinja's AI integration across the engineering lifecycle: build unified engineering data infrastructure, deploy AI-powered tooling for design, testing, reliability, and program management, embed AI Fellows into teams, drive change management and adoption, and deliver measurable outcomes tied to faster, higher-quality product development.

Lead AI integration across People & Culture by building data foundations, deploying AI capabilities and workflows, managing cross-functional AI initiatives, driving adoption and measurable impact, and transforming HR service delivery, hiring, and internal storytelling. Operate as a strategic player/coach, prioritizing execution and scaling enterprise AI solutions.