# Research Engineer, Infrastructure

**Company**: Harvey
**Location**: Remote
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $230K - $340K
**Category**: Engineering
**Industry**: Technology

**Apply**: https://jobs.ashbyhq.com/harvey/9b63e15d-2d43-4c4a-94be-75908e942ea7?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_6af1f380-8ac

## Description

At Harvey, we're transforming how legal and professional services operate by combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise.

As a Research Engineer, Infrastructure in Harvey Research, you'll build the systems that power how we adapt and improve leading open-weight models for legal work.

## Responsibilities

- Scale our post-training stack, supporting SFT and RL on leading open-weight models.

- Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks.

- Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation.

- Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability.

- Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result.

- Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments.

- Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely.

- Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation.

## Requirements

- Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation.

- Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks.

- Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack.

- Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus.

- Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve.

## Skills

### Required
- Python
- PyTorch
- JAX
- ML infrastructure
- distributed systems

### Nice to have
- RL rollouts
- agent environments
- sandboxed tool execution

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Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/harvey/9b63e15d-2d43-4c4a-94be-75908e942ea7?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)