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At Harvey, we’re transforming how legal and professional services operate — not incrementally, but end-to-end. 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. With 1000+ customers in 58+ countries, 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.
Job Responsibility:
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
Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development
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
Improve our observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions
Stay current on new developments in agentic systems and bring those learnings back to the products we build
Requirements:
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
Humble and adaptable about code and frameworks. We expect you to drive adoption of new best practices as they develop
3+ years (post-BS/MS) of software engineering experience
Proficiency in Python and experience working with LLM APIs and agent frameworks
Experience with shipping user-facing products, either on the backend or full-stack