Staff Software Engineer, AI Platform
Harvey · San Francisco, CA
- On site
- Full-time
- $300,000 / year
- San Francisco, CA
Job highlights
- Build foundational AI platform for legal services.
- Develop model routing, context, and evaluation systems.
- Design and build internal platforms and SDKs.
- Lead technical initiatives in AI and agentic systems.
- Shape the future of AI in professional services.
About the role
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. With 1500+ customers in 60+ 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.
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
Harvey’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.
Key skills/competency
- Staff Software Engineer
- AI Platform
- Backend Systems
- AI/ML Engineering
- Model Integration
- Agent Infrastructure
- Evaluation Frameworks
- Platform Abstractions
- Developer Experience
- Technical Leadership
Skills & topics
- Software Engineer
- AI Engineer
- Platform Engineer
- Backend Engineer
- Machine Learning
- Artificial Intelligence
- Agentic AI
- Infrastructure
- Python
- Cloud Computing
How to get hired
- Tailor your resume: Highlight backend systems, AI/ML, and platform/SDK experience.
- Showcase leadership: Emphasize technical leadership and experience with multi-model AI systems.
- Demonstrate ownership: Focus on examples of driving projects and navigating ambiguity.
- Prepare for technical interviews: Be ready to discuss AI infrastructure, abstractions, and distributed systems.
- Research Harvey's mission: Understand their impact on legal and professional services.
Technical preparation
Behavioral questions
Frequently asked questions
- What specific AI technologies are used at Harvey for the AI Platform role?
- While the exact stack evolves, the Staff Software Engineer, AI Platform role focuses on building the core infrastructure. This includes experience with multi-model or multi-provider AI systems, context management, session state, and memory systems. You'll be integrating and routing various foundation models, and developing evaluation frameworks. Familiarity with agent/function-calling architectures is also a plus.
- What is the career growth potential for a Staff Software Engineer at Harvey?
- Harvey emphasizes personal, professional, and financial growth. As a Staff Software Engineer on the AI Platform team, you'll be at the forefront of building a new category, offering significant opportunities to shape the technology and your career trajectory. Early-stage roles in a hyper-growth company often lead to expanded responsibilities and leadership opportunities.
- How does Harvey ensure AI accuracy in high-stakes domains like legal services?
- Accuracy is paramount at Harvey, especially in the legal domain where there is zero margin for error. The AI Platform team plays a crucial role by building robust evaluation infrastructure and frameworks. This allows all teams to systematically measure and improve AI quality, ensuring reliable performance for critical tasks.
- What is the company culture like at Harvey for engineers?
- Harvey's culture is defined by Decisiveness, Simplicity, and 'Job's Not Finished'. Engineers are expected to move fast, take ownership, operate with intensity, and push for excellence. It's a collaborative environment focused on building a generational company, with a strong emphasis on developer experience and pragmatic shipping.
- What kind of mentorship or support can I expect as a Staff Software Engineer at Harvey?
- As an early member of the AI Platform team, you'll have a significant impact on the technology and product direction. While formal mentorship structures might be developing in a hyper-growth startup, the culture encourages close collaboration, mutual learning, and pushing each other for excellence. You'll likely work closely with experienced leaders and peers.
- How does Harvey approach the integration of new AI models into its platform?
- Harvey prioritizes the rapid integration and effective routing of new foundation models. The AI Platform team owns the infrastructure that enables this capability, ensuring that product teams can leverage the best model for any given task. This agility is key to staying at the cutting edge of AI.
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