Artificial Intelligence Jobs in Cambridge, MA

Lead Foundry from inception as Co-Founder and CEO. Own venture strategy, ICP, pricing, positioning, customer development, pilot placements, go-to-market, product iteration, seed fundraising, and founding-team recruitment. Partner with FutureSight’s design and engineering teams to launch the MVP, convert early customers, build the company, and establish its culture.

Lead LucentAI from venture creation through growth as Co-Founder and CEO. Responsibilities include refining strategy, pricing, positioning, customer development, pilot conversion, product direction, fundraising, hiring, and company culture. The role requires building relationships with insurance-defense firms, shipping an AI-native litigation operations product, converting design partners into customers, and leading the seed raise with FutureSight’s support. Compensation is founder equity rather than salary.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Define the vision, strategy, and roadmap for Microsoft’s Responsible AI platform. Lead development of capabilities for AI evaluation, red teaming, safety guardrails, risk detection, monitoring, governance, transparency, and compliance. Conduct enterprise customer discovery, translate emerging risks and regulations into scalable products, and coordinate execution across engineering, research, legal, security, compliance, and Microsoft product organizations. Establish success metrics, improve adoption, and represent Microsoft with customers, partners, standards bodies, and industry forums.

Leads Microsoft sales teams and go-to-market strategies to drive revenue, cloud consumption, customer value, and AI transformation. Builds executive and partner relationships, develops market and account plans, coaches teams, manages performance, and ensures secure, compliant sales execution. The role also oversees pipeline growth, strategic partnerships, customer satisfaction, talent development, and cross-functional alignment across Microsoft business segments.

Leads Crowe’s global resource management strategy, operating model, demand and capacity planning, staffing, utilization, workforce planning, and global delivery integration. Partners with executives and cross-functional leaders to improve resource allocation, delivery economics, employee development, forecasting, dashboards, data quality, automation, and AI-enabled staffing processes. The role also establishes governance, enterprise metrics, flexible capacity practices, and continuous improvement across business units, geographies, and India delivery teams.

The Marketing Operations Lead is responsible for optimizing systems and processes in marketing and sales, enhancing demand generation efficiency, and integrating with various data systems.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead modernization of enterprise AI/ML infrastructure and analyst workflows. Build agentic, scalable, secure platform services, integrate generative AI and low-code/no-code tooling, scale AutoML for tabular and time-series, and engineer automated governance and controls while mentoring teams and influencing cross-functional stakeholders.

Lead architecture, design, and delivery of Klaviyo's Customer Agent conversational AI platform. Drive technical strategy, build scalable AI-enabled features, establish observability and reliability practices, mentor engineers, and coordinate cross-functional initiatives to deliver production agentic systems for millions of users.

Lead architecture, design, and implementation of a scalable conversational AI platform (Customer Agent). Drive technical strategy for LLM-powered agentic systems, retrieval, orchestration, memory, evaluation, and reliability. Ship AI product features, establish observability/testing/operational best practices, lead cross-functional initiatives, mentor engineers, and evaluate emerging AI technologies to deliver reliable customer-facing experiences.

As an Applied Researcher II, you will create AI systems for banking, working with cross-functional teams to develop and implement AI-powered products, while conducting applied research and engaging with emerging technologies.

The role involves partnering with a team to deliver AI products, utilizing various technologies and conducting applied research to enhance customer experiences leveraging AI.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

Lead design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

Lead development and deployment of foundational AI systems: design, build, test, and support LLM training and inference, similarity search, guardrails, observability, and optimizations for scalability, cost, latency, and throughput while shaping technical vision and collaborating with cross-functional partners.

Lead design, development, deployment, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Optimize large-scale AI performance (cost, latency, throughput), partner cross-functionally, and contribute to technical vision and roadmap.

As an Applied Researcher I, you will partner with a cross-functional team to develop AI-powered products, build AI foundation models, and conduct high-impact applied research in machine learning.

As an Applied Researcher I, you'll partner with cross-functional teams to deliver AI-powered products, build foundation models, and engage in applied research on advanced AI techniques.

The Senior Lead AI Engineer will develop and support AI software components, collaborate with cross-functional teams, and enhance banking experiences through AI systems.

Conduct applied research and build AI foundation models, particularly large language models and agentic AI systems, across design, training, evaluation, validation, and production implementation. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Research optimization, fine-tuning, robustness, explainability, and related deep learning methods while translating technical findings into business outcomes and independently pursuing impactful research agendas.

The Lead AI Engineer will design, develop, and support AI software components, leveraging various AI technologies to enhance banking services. The role involves partnering with cross-functional teams to deliver AI-powered products and optimize large-scale production AI systems.

Design, develop, test, deploy, and support production AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Develop state-of-the-art optimization techniques to improve scalability, cost, latency, throughput, and hardware utilization. Shape the long-term technical roadmap for foundational AI platforms while collaborating with engineers, research scientists, program managers, and product managers. Lead and mentor engineering teams and influence senior cross-functional stakeholders.

Participate in an accelerated consulting program delivering cloud and AI solutions for Life Sciences customers. Configure software, design workflows, structure data, conduct discovery workshops, translate business requirements into technical specifications, manage implementation scope and timelines, and guide customers through successful go-live. Collaborate with internal teams and external stakeholders while developing technical consulting, industry, and project delivery expertise.

Leads Regeneron’s pharmacovigilance systems and enablement strategy across Global Patient Safety. Owns PV systems architecture, processes, modernization roadmaps, AI use-case prioritization, regulatory compliance, validation, and data integrity. Partners with IT, AI, cloud, compliance, and functional teams to translate requirements into solutions, oversee vendors and delivery, maintain operational reliability, and drive responsible AI adoption. Builds high-performing teams and influences enterprise priorities.