Job description

WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW Harvey is redefining what’s possible in legal work by combining cutting-edge AI with intuitive product experiences. As we scale rapidly and take on increasingly complex use cases, we’re expanding our engineering team to accelerate our ability to ship fast, delight users, and build infrastructure that scales with us. As an Engineering Manager for AI Platform, you will lead and scale a team of engineers building the foundational systems that power AI across Harvey. This includes model access and routing, agent and workflow orchestration, evaluation frameworks, tool and context infrastructure, and the observability needed to run LLM-powered products reliably at scale. You will partner with Product, Research, and product engineering teams to turn fast-moving advances in applied AI into durable, reusable platform capabilities. You will also set the technical direction for how teams across Harvey build, test, and ship AI features. This role is equal parts technical depth and product intuition. You’ll be trusted to make fast, high-quality decisions, work at the cutting edge of applied AI, and grow a team that embodies engineering excellence. We move with urgency, bias toward simplicity, and expect our leaders to be hands-on when needed. If you’re energized by ownership, decisive execution, and solving real user problems, we’d love to hear from you. WHAT YOU'LL DO - Lead and grow a team of engineers building the core AI platform that powers Harvey’s products - Define the roadmap for shared AI infrastructure, including model gateways and routing, agent orchestration, tool calling, prompt and context management, and long-running job execution - Partner with Product, Research, and product engineering teams to understand their needs and turn recurring patterns into reusable, well-documented platform primitives - Build and scale evaluation, testing, and monitoring systems so teams can measure quality, catch regressions, and ship AI features with confidence - Drive reliability, latency, cost efficiency, and security across LLM-powered systems, including working with model providers on capacity, rate limits, and new capabilities - Evaluate and rapidly adopt new models, frameworks, and techniques, and decide what to build, buy, or adopt - Contribute to code and architecture reviews, unblock teams, and lead by example when needed - Hire, grow, and retain exceptional engineers who thrive in a high-trust, high-impact environment WHAT YOU HAVE - 2+ years of experience in a software engineering management role leading platform, infrastructure, or full stack product engineering teams, preferably in high-growth environments - 5+ years of experience as a backend, infrastructure, or full stack engineer - A track record of leading high-performing, engaged engineering teams - Experience building and operating production systems powered by LLMs (for example agents, retrieval, tool use, or evaluation pipelines) - Experience building internal platforms or shared services that other engineering teams depend on, and partnering with cross-functional stakeholders to prioritize them - Strong backend engineering skills in modern languages and frameworks (e.g. Python, Go, Node) and familiarity with distributed systems concepts - Sound judgment on tradeoffs among quality, latency, cost, and speed of delivery in AI systems - A plus: Experience with durable job execution frameworks, MCP or other tool and context protocols, LLM observability and eval tooling, or working directly with model providers COMPENSATION $272,000 - $355,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE harveyai.notion.site. #LI-AP1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Requirements

Must-haves

At Harvey, the future of professional services is being written today — and we’re just getting started.
This role is equal parts technical depth and product intuition. You’ll be trusted to make fast, high-quality decisions, work at the cutting edge of applied AI, and grow a team that embodies engineering excellence.
We move with urgency, bias toward simplicity, and expect our leaders to be hands-on when needed. If you’re energized by ownership, decisive execution, and solving real user problems, we’d love to hear from you.
Lead and grow a team of engineers building the core AI platform that powers Harvey’s products
Define the roadmap for shared AI infrastructure, including model gateways and routing, agent orchestration, tool calling, prompt and context management, and long-running job execution
Partner with Product, Research, and product engineering teams to understand their needs and turn recurring patterns into reusable, well-documented platform primitives
Build and scale evaluation, testing, and monitoring systems so teams can measure quality, catch regressions, and ship AI features with confidence
Drive reliability, latency, cost efficiency, and security across LLM-powered systems, including working with model providers on capacity, rate limits, and new capabilities
Evaluate and rapidly adopt new models, frameworks, and techniques, and decide what to build, buy, or adopt
Contribute to code and architecture reviews, unblock teams, and lead by example when needed
Hire, grow, and retain exceptional engineers who thrive in a high-trust, high-impact environment
2+ years of experience in a software engineering management role leading platform, infrastructure, or full stack product engineering teams, preferably in high-growth environments
5+ years of experience as a backend, infrastructure, or full stack engineer
A track record of leading high-performing, engaged engineering teams
Experience building and operating production systems powered by LLMs (for example agents, retrieval, tool use, or evaluation pipelines)
Experience building internal platforms or shared services that other engineering teams depend on, and partnering with cross-functional stakeholders to prioritize them
Strong backend engineering skills in modern languages and frameworks (e.g. Python, Go, Node) and familiarity with distributed systems concepts
Sound judgment on tradeoffs among quality, latency, cost, and speed of delivery in AI systems
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Nice-to-haves

A plus: Experience with durable job execution frameworks, MCP or other tool and context protocols, LLM observability and eval tooling, or working directly with model providers

Application prep

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