Asks for

The ad says“Proficiency in Python and experience working with LLM APIs and agent frameworks.”
The ad says“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.”
The ad says“Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.”
The ad says“Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.”
The ad says“Improve our observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions.”
The ad says“Proficiency in Python and experience working with LLM APIs and agent frameworks.”
The ad says“Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.”
The ad says“Improve our observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions.”
The ad says“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.”
The ad says“Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.”
The ad says“Proficiency in Python and experience working with LLM APIs and agent frameworks.”
The ad says“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.”
The ad says“Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.”

Why Harvey

At Harvey, we’re transforming how legal and professional services operate. 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. We have 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.

Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.

At Harvey, the future of professional services is being written today — and we’re just getting started.

Role Overview

As a Software Engineer, Agents, you'll build the systems that make our AI agents indispensable to legal professionals.

You will design environments and actions for agentic professional work, make model selection decisions, manage context windows, create optimal tools, and develop evals that enable faster iteration loops to unlock new capabilities.

We're looking for engineers and researchers who are immersed in the space, driven to ship impactful products, and are experienced in using practical evaluations to drive task completion quality and customer delight.

What You'll Do

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.

What You Have

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.
5-8 years (post-BS/MS) of software engineering experience.
We are hiring for this role across Mid, Senior, and Staff levels.
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.

Compensation

$231,000 - $340,000 USD

Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices here.

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Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai