# Research Infrastructure Engineer

**Company:** [Thinking Machines Lab](https://hotfix.jobs/companies/thinking-machines-lab)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $350k – $475k/yr
**Skills:** Python, Rust, PyTorch, JAX, Ray, Spark, Distributed Systems, Ml Research Infrastructure, Experiment Tracking, Evaluation Frameworks
**Posted:** 2026-07-01

> Build and operate research infrastructure like evaluation frameworks, RL training systems, and experiment tracking platforms. Partner directly with ML researchers to identify bottlenecks, ensure high adoption of tools, and accelerate research velocity.

## Job Description

## What You'll Do
- Design, build, and operate research infrastructure including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.
- Develop high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessment.
- Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs. Implement monitoring and observability.
- Partner directly with researchers to identify bottlenecks and unlock new capabilities. Own research tooling like a product manager, proactively seeking feedback and tracking adoption.
- Collaborate with infrastructure, data, and product teams to integrate tools across the technical stack.

## Skills and Qualifications
**Minimum qualifications:**
- Bachelor's degree or equivalent experience in computer science, engineering, machine learning, or similar.
- Strong software engineering fundamentals with a track record of building reliable, maintainable systems.
- Proficiency in at least one backend language (we use Python or Rust).
- Comfort operating across the stack and owning projects end-to-end.
- Experience in highly collaborative environments involving many different cross-functional partners and subject matter experts.

**Preferred qualifications:**
- Track record building tooling for researchers that achieved high adoption without top down mandates.
- Experience building or maintaining ML research infrastructure such as training frameworks, evaluation libraries, or experiment tracking systems.
- Contributions to open-source ML tools or widely-used internal frameworks at research-focused organizations.
- Record of publications or technical writing on ML systems, infrastructure, or tooling.
- Background working closely with ML researchers to understand and solve their tooling needs.
- Familiarity with distributed systems, modern ML frameworks (PyTorch, JAX), and data processing at scale.
- Experience with research observability tools, distributed compute frameworks (Ray, Spark), or large-scale evaluation pipelines.

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