Research Engineer, Infrastructure, Training Systems

Thinking Machines Lab•San Francisco, CA

4d•$350,000 - $475,000•Onsite

About The Position

We’re looking for an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Thinking Machines fast and reliable to ensure our research teams can focus on science, not system bottlenecks. This role is ideal for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale. You’ll take ownership of the training stack end to end, ensuring every GPU cycle drives scientific progress. This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

Requirements

Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Nice To Haves

Past experience working on distributed training for the world’s largest models to make them stable, reliable, and performant.
Track record of improving research productivity through infrastructure design or process improvements.
Contributions to open-source ML infrastructure such as PyTorch, XLA, Megatron-LM, or DeepSpeed.

Responsibilities

Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large-scale training workloads.
Develop high-performance optimizations to maximize throughput and efficiency.
Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.
Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration.
Collaborate with researchers and engineers to build scalable infrastructure.
Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.