Software Engineer, Infrastructure

Thinking Machines Lab•San Francisco, CA

1d•Onsite

About The Position

Thinking Machines is hiring a Software Engineer, Infrastructure to design and build distributed systems powering its model training and serving platforms. The role supports systems ranging from clusters used to train frontier models with Inkling to the multi-tenant serving infrastructure behind Tinker. It is a foundational infrastructure position at a fast-moving startup, with ownership over large-scale systems that affect how quickly research and product teams can iterate. The engineer will build, operate, and improve infrastructure for large-scale model training and inference across thousands of accelerators. The work includes orchestration, scheduling, storage, resource management, reliability, performance, and observability. The role also involves partnering with researchers and platform engineers, debugging distributed failures across the infrastructure stack, and maintaining internal libraries and APIs primarily in Python and Go.

Requirements

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.

Nice To Haves

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, such as Kubernetes, and infrastructure-as-code.
Contributions to open-source infrastructure projects.
Comfort working with high autonomy in a fast-changing, early-stage environment.

Responsibilities

Design, build, and operate distributed systems supporting large-scale model training and inference across thousands of accelerators.
Build and maintain core infrastructure, including orchestration, scheduling, storage, and resource management systems.
Improve infrastructure reliability, performance, and observability across research and product teams.
Partner with researchers and platform engineers to understand infrastructure needs and develop robust, well-abstracted systems.
Debug and resolve complex distributed failures across networking, storage, compute, and scheduling.
Write and maintain internal libraries and APIs, primarily in Python and Go.