Research Engineer, Infrastructure, Inference

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, optimize, and scale the systems that power large AI models. Your work will make inference faster, more cost-effective, more reliable, and more reproducible to enable our teams to focus on advancing model capabilities rather than managing bottlenecks. Our focus is on performant and efficient model inference both to power real-world applications and to accelerate research. This role is responsible for the infrastructure that ensures every experiment, evaluation, and deployment runs smoothly at scale. 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, engineering, or similar.
Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
Experience with inference serving systems optimized for throughput and latency (e.g., SGLang, vLLM).
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.
Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases

Nice To Haves

Experience training or supporting large-scale language models with hundreds of billions of parameters or more.
Understanding of distributed compute systems, GPU parallelism, and hardware-aware optimizations.
Contributions to open-source ML or systems infrastructure projects (e.g., SGLang, vLLM, PyTorch, Triton, DeepSpeed, XLA).
Track record of improving research productivity through infrastructure design or process improvements.

Responsibilities

Work alongside researchers and engineers to bring cutting-edge AI models into production.
Collaborate with research teams to enable high-performance inference for novel architectures.
Design and implement new techniques, tools, and architectures that improve performance, latency, throughput, and efficiency.
Optimize our codebase and compute fleet (e.g., GPUs) to fully utilize hardware FLOPs, bandwidth, and memory.
Extend orchestration frameworks (e.g., Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving.
Establish standards for reliability, observability, and reproducibility across the inference stack.
Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.