Software Engineer, Research Tools

Posted 10 Days Ago
Be an Early Applicant
San Francisco, CA, USA
Hybrid
300K-475K Annually
Junior
Artificial Intelligence • Information Technology
The Role
Build and maintain research infrastructure, including evaluation frameworks, training systems, experiment tracking, data pipelines, and visualization tools. Partner directly with researchers to identify bottlenecks, improve productivity, and deliver reliable end-to-end systems. Develop reproducibility, traceability, quality control, monitoring, and observability capabilities while treating research tooling as a product.
Summary Generated by Built In
About Thinking Machines

The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.

About the Role

We are a team of full stack generalists with strong product instincts who work closely with researchers. We build systems that compound research and engineering velocity over time. We own the internal platform researchers use every day to manage and monitor training runs and evaluations, inspect and debug model trajectories, and compare results on shared leaderboards.

You’ll own key parts of this platform, including evaluation and training libraries, experiment-tracking systems, and visualization tools. You’ll identify researchers’ most important bottlenecks and turn them into reliable, generalizable systems. Our team is still small—expect to participate in research meetings, build close relationships with researchers, and gather feedback frequently to develop conviction about where we should invest next.

This role requires technical judgment, close cross-functional collaboration, and product intuition. Success means researchers trust your systems to work, rely on them every day, and find them genuinely delightful to use.

What You’ll Do
  • Design, build, and maintain research infra, including evaluation frameworks, training systems, experiment tracking platforms, and visualization tools.

  • Work across backend systems, data pipelines, and user-facing applications to deliver tools end to end.

  • Partner directly with researchers to identify bottlenecks and unlock new capabilities. Treat research tooling as a product: proactively gather feedback, set priorities, and measure adoption.

  • Build systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, with monitoring and observability built in.

Skills and QualificationsMinimum 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.

  • 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 working with databases, data warehouses (Clickhouse), caching systems such as Redis, and other data infra.

  • 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.

Preferred qualifications

We encourage you to apply even if you meet only some of these:

  • A track record of building tools for researchers, improving developer productivity, or creating technical products for technical users.

  • Experience building polished, intuitive user-facing applications that demonstrate strong product judgment and attention to detail on UI/UX.

  • Experience at a startup or on a small team, building technically complex products end to end.

  • Experience building or maintaining ML research infrastructure, such as training frameworks, evaluation libraries, or experiment-tracking systems.

  • Experience working closely with researchers to understand and solve their tooling needs.

Logistics
  • Location: This role is based in San Francisco, California.

  • Compensation: 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 success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

  • Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Skills Required

  • Bachelor's degree or equivalent practical experience in computer science, engineering, machine learning, or a related field
  • Two years of post-graduate experience as a software engineer or ML engineer, excluding internships
  • Strong software engineering fundamentals and experience building reliable, maintainable systems
  • Proficiency in at least one backend programming language, such as Python or Rust
  • Experience with React and TypeScript
  • Experience with databases, data warehouses such as ClickHouse, caching systems such as Redis, and other data infrastructure
  • Experience working across the stack and owning projects from discovery through deployment and operation
  • Experience collaborating with cross-functional partners and subject-matter experts
  • Experience building tools for researchers, improving developer productivity, or creating technical products for technical users
  • Experience building polished, intuitive user-facing applications with strong product judgment and UI/UX attention to detail
  • Experience at a startup or on a small team building technically complex products end to end
  • Experience building or maintaining ML research infrastructure, including training frameworks, evaluation libraries, or experiment-tracking systems
  • Experience working closely with researchers to understand and solve tooling needs
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The Company
HQ: Singapore
91 Employees

What We Do

Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable. We are scientists, engineers, and builders who've created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.

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