Software Engineer, Infrastructure
Thinking Machines Lab1 month ago
Base Salary
$300k - $400k/yr
Responsibilities
- Design, build, and operate distributed systems for large-scale model training and inference across thousands of accelerators.
- Build and maintain 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 translate infrastructure needs into 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.
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.
- Strong grounding in distributed systems fundamentals, including consensus, consistency, fault tolerance, and networking.
- Preferred experience with ML infrastructure such as training orchestration, job schedulers, or distributed storage and data systems.
- Preferred experience operating large-scale GPU or TPU clusters.
- Preferred experience with Kubernetes and infrastructure-as-code.
- Open-source infrastructure contributions are preferred.
- Ability to work with high autonomy in a fast-changing, early-stage environment.
Benefits
- Annual salary range of $300,000-$400,000 USD.
- Generous health, dental, and vision benefits.
- Unlimited paid time off and paid parental leave.
- Relocation support as needed.
- Based in San Francisco, California.
- The company sponsors visas and is committed to working through the visa process with qualified candidates, though success is not guaranteed for every candidate or role.
Tech Stack
Categories
About Thinking Machines Lab
Thinking Machines Lab develops AI and generative AI software and conducts applied research to help organizations make data-driven decisions. The company builds products and data science solutions for enterprise use cases, pairing foundational models with practical tooling and services across industries. It is privately held and headquartered in San Francisco.