Research Engineer, Infrastructure, Inference

Bison; San Francisco (CA); On-site; USD 400,000 - 500,000; Full time

Benefits offered by this job

Health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Job summary

Thinking Machines, based in San Francisco, CA, seeks an infrastructure research engineer to design, optimize, and scale systems powering large AI models. You will improve inference speed, cost-effectiveness, reliability, and reproducibility to accelerate model capabilities.

You’ll work with researchers and engineers to deploy cutting-edge AI models, extend orchestration (Kubernetes, Ray, SLURM) for distributed inference, and publish learnings to advance scalable AI infrastructure.

Qualifications

• Bachelor’s degree in computer science, engineering, or related field.
• Experience with deep learning frameworks and system architectures.
• Exposure to inference serving systems optimized for latency and throughput.
• Collaborative experience with cross-functional teams.
• Proactive, action-oriented mindset across multiple stacks.
• Strong coding and debugging skills in complex codebases.

Responsibilities

• Collaborate with researchers and engineers to productionize cutting-edge AI models.
• Enable high-performance inference for new architectures.
• Design and implement tooling to improve performance, latency, and throughput.
• Optimize codebase and compute fleets (GPUs) for better utilization.
• Extend orchestration frameworks for distributed inference and large-batch serving.
• Establish reliability, observability, and reproducibility across the inference stack.
• Publish learnings through internal docs, open-source libraries, or reports.

Skills

Deep learning frameworks

Inference serving

Collaboration

Initiative

Debugging

Education

Bachelor’s degree or equivalent

Tools

PyTorch

JAX

SGLang

vLLM

Kubernetes

Ray

SLURM

Job description

About Thinking Machines

The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.

About the Role

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.

What You’ll Do

• 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.

Skills and Qualifications

Minimum qualifications:

• 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

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• 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.

Logistics

• Location: This role is based in San Francisco, California.

• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $400,000 - $500,000 USD.

• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.