Role OverviewHarvey's products all depend on a shared AI foundation: the model layer and agent infrastructure that determine the quality of work our agents deliver. Legal is one of the hardest domains for AI: documents run to hundreds of pages, matters can span millions of files, and there is zero margin for error on accuracy.
The AI Platform team builds the foundation that every product and agent team at Harvey builds upon. This team is early and there's a lot to build: model routing, agent architecture, context management, evals. Your work here sets the ceiling for what Harvey's AI can do.
Representative Projects- Context Engineering & Agent Infrastructure. Build the platform-level systems for context management, session state, and memory that all of Harvey's agents and products rely on.
- Model Integration & Routing. Own the infrastructure that lets Harvey onboard new foundation models fast and route to the right one for every task - a capability every product team depends on.
- Evaluation Infrastructure. Build the shared eval tooling and frameworks that let every team across Harvey measure and improve AI quality systematically.
- Shared Abstractions. Create the SDKs, platform primitives, and developer tooling that make it dramatically easier for product teams to ship AI-powered features.
What You'll Do- Design and build abstractions and platform-level systems that improve all of Harvey's agentic products.
- Own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context.
- Build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively.
- Partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products.
- Evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge.
What You Have- 8+ years of experience building backend systems, with at least 1+ year focused on AI/ML engineering and a track record of technical leadership across teams.
- Experience building and shipping multi-model or multi-provider AI systems in production.
- Familiarity with context management, session state, or memory systems in AI or distributed systems. You've thought about what the model sees and why it matters.
- A track record of building internal platforms, SDKs, or shared infrastructure that other engineering teams actually adopted - and an understanding of why developer experience matters as much as raw capability.
- Strong judgment about abstractions. Opinionated about good design but pragmatic about shipping incrementally.
- Excitement about agentic AI and the infrastructure challenges of making autonomous systems reliable when the stakes are real.
- A bias toward full ownership: you navigate ambiguity well and don't wait for a roadmap to start solving problems.
- Bonus: experience building evaluation frameworks, working with agent/function-calling architectures, familiarity with legal or other high-stakes professional services domains, or time at early-stage or hyper-growth startups where the underlying technology changes regularly.
Compensation Range$231,000 - $340,000 USD
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