# Senior Software Engineer, AI Platform

**Company:** [Harvey](https://hotfix.jobs/companies/harvey)
**Location:** San Francisco, CA, New York, NY
**Role:** ML Engineering
**Salary:** $193k – $290k/yr
**Experience:** 5+ years
**Skills:** ai/ml engineering, Backend Systems, multi-model ai systems, model integration, model routing, Evaluation Frameworks, context management, agent architecture, SDKs, platform infrastructure
**Posted:** 2026-07-16

> Build core AI platform infrastructure at Harvey including model routing, context management, agent architecture, and shared evaluation frameworks that power all agentic legal AI products. Requires 5+ years backend experience with 1+ year in AI/ML, production multi-model systems, and platform-building track record.

## Job Description

## 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.

## Requirements
- 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.
- Track record of building internal platforms, SDKs, or shared infrastructure that other engineering teams actually adopted.
- 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.
- Bias toward full ownership: navigate ambiguity well and don’t wait for a roadmap to start solving problems.

## Nice-to-Haves
- Experience building evaluation frameworks.
- Working with agent/function-calling architectures.
- Familiarity with legal or other high-stakes professional services domains.
- Time at early-stage or hyper-growth startups where the underlying technology changes regularly.

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