Software Engineer, Research Acceleration

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. 

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.

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.

As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.