Software Engineer, Systems Generalist at Thinking Machines Lab

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’re looking for generalist infrastructure and systems engineers to help build the systems that power our foundation models and the internal teams on research and product development to be able to create the models and ship the products powered by our models.

You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind everything we do. You’ll work across the full technical stack, solving complex distributed systems problems and building robust, scalable platforms.

Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, improve infrastructure efficiency, and enable key insights across our models, products, and data assets.

WHAT YOU’LL DO

We interview generally, but during project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the infrastructure teams where they'll have the greatest impact and growth potential.

Here are example areas you may contribute to depending on your area of expertise and interest:

- Core Infrastructure: We support teams that train, research, and ultimately serve AI models and build the underlying infrastructure for the clusters to reliably and safely train frontier models. Examples might include building systems and running large Kubernetes clusters with GPU workloads, or building infrastructure to support Tinker.

- Data Infrastructure: We build and maintain the data systems for our research and products. You'll design and optimize data pipelines using tools like Spark and other modern data infrastructure technologies.  You’ll build scalable, reliable, data infrastructure while embedding governance best practices.

- Developer Productivity: We care deeply about research and engineering productivity and our ability to continue shipping quickly. We build tooling, systems, frameworks, and systems to make sure everyone gets well configured, optimized developer environments.

SKILLS AND QUALIFICATIONS

MINIMUM QUALIFICATIONS

- Bachelor’s degree or equivalent experience in computer science, engineering, or similar.

- Proficiency in at least one backend language (we use Python or Rust).

- Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).

- Comfort operating across the stack and owning projects end-to-end.

- Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.

- A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

PREFERRED QUALIFICATIONS

- Strong debugging across application, OS, and network layers.

- Proficiency in Python or Rust (or similar), containers, and modern CI.

- Experience with Kubernetes, controllers/operators, or performance profiling.

- Familiarity with GPU/ML workflows or large‑scale data/eval pipelines.

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 - $400,000 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.