Software Engineer, Distributed Systems

Thinking Machines Lab•San Francisco, CA

4d•$300,000 - $350,000•Onsite

About The Position

We're hiring a Software Engineer, Distributed Systems to design and build the core distributed systems that everything else at Thinking Machines runs on — orchestration, scheduling, storage, and networking across thousands of machines. Your work underpins both Inkling's training clusters and Tinker's serving platform, and shows up anywhere we need software to coordinate reliably at scale. This is deep systems work. You'll be reasoning about consensus, fault tolerance, and performance under real-world failure conditions, often on problems that don't have an off-the-shelf solution.

Requirements

5+ years of experience building large-scale distributed systems
Proficiency in Python and Go, C++, or another systems-level language
Strong understanding of distributed systems fundamentals: consensus, consistency, replication, and fault tolerance
Experience with network programming, load balancing, or distributed storage systems

Nice To Haves

Experience building distributed compute or orchestration systems for AI or ML workloads
Fluent in containerization, orchestration, and distributed compute frameworks
Experience with specialized hardware (GPUs, TPUs) and their integration into distributed training or serving systems
Background at AI research labs, high-performance computing centers, or similarly demanding environments
Published work or open-source contributions related to distributed systems or performance engineering
Comfortable operating with high autonomy in a fast-changing, early-stage environment

Responsibilities

Design and build distributed systems for compute orchestration, scheduling, storage, and networking across large GPU and TPU clusters
Develop fault-tolerant systems that keep running correctly as hardware fails, networks partition, and workloads scale
Build the distributed storage and data orchestration layers that move and persist large volumes of training and model data
Improve the performance and efficiency of collective communication, scheduling, and resource allocation across thousands of machines
Partner with research and infrastructure teams to identify systems bottlenecks and design solutions from first principles
Write production-quality code and help shape the architecture of systems used company-wide