The mission of Thinking Machines is to build AI that extends human will and judgment.
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.
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.
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.
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.
Will AI replace this role?
Check AI displacement risk, transferable skills, and career alternatives for Software Engineer, Research Acceleration
The salary range for the Software Engineer, Research Acceleration position at Thinkingmachines is USD 350k-475k/yr.
The Software Engineer, Research Acceleration position at Thinkingmachines is based in San Francisco. This is an on-site position.
The Software Engineer, Research Acceleration position at Thinkingmachines is a Full time role.
Key skills for the Software Engineer, Research Acceleration position at Thinkingmachines include: machine learning, python, rust, pytorch.
