About the job
At Thinking Machines Lab, we are dedicated to empowering humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone can access the knowledge and tools necessary to harness AI for their unique needs.
Our diverse team of scientists, engineers, and builders has developed some of the most recognized AI products, including ChatGPT and Character.ai, as well as notable open-weight models like Mistral, and popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.
About the Role
We are currently seeking versatile infrastructure and systems engineers to help construct the foundational systems that support our models and facilitate research and product development. Your contributions will enable teams to create and deliver groundbreaking AI products.
As a member of a small, high-impact team, you will be responsible for architecting and scaling the core infrastructure that underpins our operations. This role involves working across the entire technical stack, addressing complex distributed systems challenges, and developing robust, scalable platforms.
Infrastructure is vital to our success; it serves as the foundation for every innovation. You will collaborate directly with researchers to expedite experiments, enhance infrastructure efficiency, and derive critical insights from our models, products, and data assets.
Note: This is an evergreen role, meaning we are continuously accepting expressions of interest. Due to the volume of applications, there may not always be an immediate match for your skills and experience. However, we encourage you to apply. Applications are reviewed regularly, and we reach out to candidates as new opportunities arise. You may reapply if you gain additional experience, but please wait at least six months between applications. Additionally, we occasionally post specific roles for particular projects or teams, and you are welcome to apply for those as well.
What You’ll Do
Interviews will be conducted in a general manner, but project selection will consider your interests and experience alongside the needs of the organization. This flexible approach allows us to align talented engineers with the infrastructure teams where they will have the greatest influence and opportunities for growth.
Depending on your expertise and interests, you may contribute to various areas such as:
- Core Infrastructure: Supporting teams that train, research, and ultimately serve AI models by building the infrastructure required for reliable and secure training of frontier models. This may include developing systems and managing large Kubernetes clusters with GPU workloads.

