Software Engineer, Agents

Harvey Harvey · AI Frontier · San Francisco, CA · Engineering

Software Engineer, Agents role at Harvey, focusing on building AI agent systems for legal professionals. Responsibilities include designing environments and actions for agents, managing context windows, creating tools, and developing evaluations to improve iteration loops and task completion quality. The role involves optimizing agent performance through prompt engineering, model selection, tool design, and working with model infra teams on low-latency execution and observability. Requires Python proficiency, experience with LLM APIs and agent frameworks, and shipping user-facing products.

What you'd actually do

  1. Partner with customers and PMs to understand legal workflows, design practical evaluations that capture what “excellent” means, and ship agents that get the job done.
  2. Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.
  3. Work with our model infra team to design and implement infrastructure for low-latency agent execution, including caching strategies, parallel tool calls, or subagent patterns.
  4. Improve our observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions.
  5. Stay current on new developments in agentic systems and bring those learnings back to the products we build.

Skills

Required

  • Python
  • LLM APIs
  • agent frameworks
  • shipping user-facing products
  • prompt engineering
  • model selection
  • tool design
  • skill writing
  • context window management
  • eval harness development
  • low-latency agent execution
  • caching strategies
  • parallel tool calls
  • subagent patterns
  • observability
  • instrumentation

Nice to have

  • legal workflows
  • domain-specific agents

What the JD emphasized

  • build the systems that make our AI agents indispensable
  • design environments and actions for agentic professional work
  • make model selection decisions
  • create optimal tools
  • develop evals that enable faster iteration loops to unlock new capabilities
  • immersed in the space
  • driven to ship impactful products
  • experienced in using practical evaluations to drive task completion quality and customer delight
  • automating information requests and diligence checks across hundreds of thousands of files with retrieval and file editing agents
  • improving the latency and quality of agents on applying a standard legal "playbook" to contracts
  • optimizing our multi-source retrieval agents
  • tuning the harness and libraries for coding agents
  • Passion for building effective domain-specific agents
  • Iterative mindset: you develop proof of concepts, make decisions quickly, and ship v0s
  • Comfortable with when and how to use evaluations to drive quality
  • Proficiency in Python and experience working with LLM APIs and agent frameworks
  • Experience with shipping user-facing products

Other signals

  • agentic AI
  • enterprise-grade platform
  • product-market fit
  • scaling fast
  • defining a new category