Software Engineer, AI Platform

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

• 5+ years of experience building backend systems, with at least 1+ year focused on AI/ML engineering. Staff candidates will typically have 8+ years 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

$220,000 - $300,000