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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Product Manager, Legal Operations Platform - **Company:** Harvey, Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $213,600.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software as a Service, Document Management Systems, Distributed Systems, Knowledge Management, Data Pipelines - **Published:** July 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a75c9f62fb94ef3f ## About the Role * 7+ years of product management experience, with at least 3 years at a Staff or Senior PM level at a high-growth technology company. * Proven track record of owning and scaling complex platform or infrastructure products, ideally in enterprise SaaS, document management, knowledge management, or data-intensive domains. * Strong technical acumen with the ability to engage deeply with engineering on system design, distributed systems, data pipelines, and retrieval architectures. * Experience building products that handle sensitive data with robust security, compliance, and access control requirements. * Excellent communication skills with the ability to influence stakeholders at various levels, from engineers to executives. * Demonstrated ability to thrive in ambiguous, fast-paced environments and drive clarity through complexity. * Strong product sense and attention to detail-ability to think through both high-level strategy and nitty-gritty implementation details. ## Description We're looking for a senior product leader who will own and drive the 0 to 1 strategy for Harvey's legal operations platform - the feature set that is quickly becoming the premier admin experience for in-house legal teams, giving General Counsel and legal operations leaders visibility and control over how their department engages, governs, and evaluates the outside firms and vendors it relies on. This person joins the Product team, whose mission is to transform how legal professionals interact with AI to deliver faster, higher-quality outcomes at scale. The role directly enables Harvey's enterprise expansion by giving large legal departments the operational tooling they need to adopt Harvey with confidence - a critical unlock for procurement-led and compliance-driven buyers. It sits on one of Harvey's highest-impact roadmaps, with a level of ambition that has generated real excitement across the legal industry, from in-house teams to the outside firms they work with. They will partner with engineering, design, go-to-market, and customer success to define the roadmap and shipping cadence for one of Harvey's fastest-growing product surfaces. It is a rare opportunity to define from first principles how AI-native software reshapes a decades-old legal operations workflow. This is a highly strategic role at the intersection of platform, product, and partnerships. You'll work closely with engineering, design, partnerships, and GTM teams to build capabilities that are not just functional, but transformative - making Harvey the system of intelligence for professional work., * Define and own the product roadmap for Harvey's legal operations platform - from how legal departments configure and govern their relationships with outside firms and vendors to performance visibility and compliance - ensuring it aligns with Harvey's enterprise strategy. * Drive end-to-end execution of features from discovery through launch, working closely with engineering to ship on a fast cadence while maintaining a high quality bar. * Partner with enterprise customers and customer success to deeply understand how legal departments manage their relationships with outside firms and vendors today, translating those insights into product requirements that unlock adoption at scale. * Collaborate cross-functionally with go-to-market, partnerships, and legal engineering to ensure these features are positioned and enabled effectively for sales and deployment. * Establish success metrics and feedback loops that quantify the value the platform delivers to customers, using data to continuously prioritize and iterate. ## Related Videos - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Best Practices for AI-Assisted Development of Distributed Systems](https://www.wearedevelopers.com/videos/100200-best-practices-for-ai-assisted-development-of-distributed-systems) - [Same Tower, New Confusion: The Tower of Babel 2.0](https://www.wearedevelopers.com/videos/100101-same-tower-new-confusion-the-tower-of-babel-2-0) - [Marketing x Product: How We Stopped Gaslighting Each Other and Built AI Products That Actually Work](https://www.wearedevelopers.com/videos/100244-marketing-x-product-how-we-stopped-gaslighting-each-other-and-built-ai-products-that-actually-work) - [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes) - [Stop Renaming Teams, Start Product Thinking: A PM's Guide to Platform-as-a-Product](https://www.wearedevelopers.com/videos/100342-stop-renaming-teams-start-product-thinking-a-pm-s-guide-to-platform-as-a-product) ## Related Articles - [13 AI Tools You Have to Try](https://www.wearedevelopers.com/magazine/219-13-ai-tools-you-have-to-try) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Why Your AI Tool Fails After the Demo](https://www.wearedevelopers.com/magazine/704-why-your-ai-tool-fails-after-the-demo)