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 RoleWe're hiring a Software Engineer, Infrastructure to design and build the distributed systems that power our model training and serving platforms. You'll work on the systems underlying everything we do — from the clusters that train our frontier models with Inkling, to the multi-tenant serving infrastructure behind Tinker.
This is a foundational infrastructure role at a fast-moving startup. You'll have real ownership over systems that run at large scale, and your work will directly determine how quickly our research and product teams can iterate.
What You'll DoDesign, build, and operate distributed systems that support large-scale model training and inference across thousands of accelerators
Build and maintain core infrastructure, including orchestration, scheduling, storage, and resource management systems
Improve the reliability, performance, and observability of infrastructure used across research and product teams
Partner with researchers and platform engineers to understand infrastructure needs and turn them into robust, well-abstracted systems
Debug and resolve complex distributed failures across the stack, from networking and storage to compute and scheduling
Write and maintain internal libraries and APIs, primarily in Python and Go, that other engineers build on
Demonstrated expertise designing and developing large-scale distributed systems
Strong proficiency in Python and Go
Experience building, deploying, and operating production infrastructure at scale
Solid grounding in distributed systems fundamentals, such as consensus, consistency, fault tolerance, and networking
Experience with ML infrastructure, such as training orchestration, job schedulers, or distributed storage and data systems
Experience operating large-scale GPU or TPU clusters
Experience with container orchestration (e.g. Kubernetes) and infrastructure-as-code
Contributions to open-source infrastructure projects
Comfortable working with high autonomy in a fast-changing, early-stage environment
Location: This role is based in San Francisco, CA.
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.
Skills Required
- Expertise designing and developing large-scale distributed systems
- Strong proficiency in Python and Go
- Experience building, deploying, and operating production infrastructure at scale
- Knowledge of distributed systems fundamentals, including consensus, consistency, fault tolerance, and networking
- Experience with ML infrastructure, including training orchestration, job schedulers, or distributed storage and data systems
- Experience operating large-scale GPU or TPU clusters
- Experience with container orchestration, such as Kubernetes, and infrastructure as code
- Contributions to open-source infrastructure projects
- Ability to work autonomously in a fast-changing, early-stage environment
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.
