Software Engineer, Research Acceleration

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health benefits

Dental benefits

Vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. is seeking engineers to build the libraries and tools that accelerate AI research.

You will own internal infrastructure, including evaluation libraries, RL training libraries, and experiment tracking platforms, and design scalable systems that boost research velocity. You will work directly with researchers to remove bottlenecks, ensure reproducibility, and deliver reliable tooling.

Qualifications

• Bachelor's degree or equivalent in CS, engineering or ML.
• Strong software engineering fundamentals applicable to reliable systems.
• Backend proficiency in Python or Rust and end-to-end ownership.

Responsibilities

• Design, build, and operate research infrastructure including evaluation frameworks and RL training systems.
• Develop high-throughput, scalable pipelines for distributed evaluation and multimodal assessment.
• Build reproducible, observable systems with quality controls across experiments.
• Partner with researchers to identify bottlenecks and drive adoption of tooling.
• Collaborate with infra, data, and product teams to integrate tools across the stack.

Skills

Software engineering fundamentals

Backend development (Python or Rust)

Collaborative work

Education

Bachelor's degree or equivalent in CS/Engineering/ML

Job description

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

About the Role

We're looking for engineers to build the libraries and tools that accelerate research at Thinking Machines. You'll own internal infrastructure - evaluation libraries, RL training libraries, experiment tracking platforms - and build systems that compound research velocity over time.

This is a collaborative role. You will work directly with researchers to identify bottlenecks and pain points. Success means researchers trust your systems to just work and find them a delight to use.

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 - we encourage you to apply if you meet some but not all of these:

• 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.

Logistics

• Location: This role is based in San Francisco, California or New York, NY.
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $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.