Software Engineer, Evaluation Platform / Infra

The mission of Thinking Machines is to build AI that extends human will and judgment.

Evaluation is one of the most important pillars of building frontier AI systems. It guides research direction, powers experimentation, and helps us understand whether changes to data and training are improving the capabilities and behaviors we care about. To support this work, researchers need a powerful, self-serve platform that makes it easy to author evaluations, run them or reproduce them reliably at scale, and extract insight from the results. The platform must support both standardized external benchmarks and fast-moving internal evaluations, many kinds of tasks and graders, and inspection from aggregate metrics down to individual model trajectories. In this role, you will design and build this platform end to end. You will work across Python frameworks, data pipelines, APIs, and user-facing applications, and collaborate closely with pre-training, post-training, and applied teams to improve how we evaluate models and turn results into research decisions.

Design, build, and maintain the platform for authoring, running, tracking, and analyzing model evaluations that are critical in day to day work and model releases. Work across evaluation libraries, distributed backend systems, data pipelines, APIs, and user-facing applications to deliver capabilities end to end. Build flexible abstractions for evaluation tasks, environments, graders, datasets, and model outputs without constraining fast-moving research. Make evaluation results reproducible and trustworthy through versioning, provenance, observability, failure recovery, and robust quality controls. Partner directly with researchers to identify bottlenecks and turn bespoke workflows into self-serve systems that work across teams. Work with Research Tooling, the engineering team behind Thinking Machines’ internal research platform.

Minimum qualifications A bachelor’s degree, or equivalent practical experience, in computer science, engineering, machine learning, or a related field. Two years of post-grad work experience as a software engineer or ML engineer, exclusive of internships. Hands-on experience building or maintaining evaluations, benchmarks, graders, or model-quality systems for large language or multimodal models. Strong software engineering fundamentals and experience building reliable, maintainable systems. Proficiency in at least one backend programming language; we primarily use Python and Rust. We use React and Typescript on the frontend. Experience with databases, data pipelines, distributed systems, or other data-intensive infrastructure. Comfort working across the stack and owning projects from initial problem discovery through deployment and operation. Experience collaborating with cross-functional partners and subject-matter experts.

We encourage you to apply even if you meet only some of these: A track record of building frameworks, SDKs, or developer tools with thoughtful abstractions and a strong user experience. Experience with distributed job execution, workflow orchestration, sandboxed environments, or large-scale data processing. Experience building polished, intuitive interfaces for inspecting complex data, comparing experiments, or debugging model behavior. Familiarity with large language or multimodal model evaluation, including model-based grading, human evaluation, or synthetic data. Experience working closely with researchers to understand their workflows and turn rapidly evolving needs into durable systems. Experience at a startup or on a small team, building technically complex products end to end. Logistics Location: This role is based in San Francisco, California.

Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $475,000 USD. Visa sponsorship: We sponsor visas. While we can't guarantee