Research Engineer, Infrastructure, Inference

Thinking Machines Lab Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 350,000 - 475,000; Full time

Benefits offered by this job

Health, dental, vision benefits

Unlimited PTO

Parental leave

Relocation support

Job summary

Thinking Machines Lab Inc. in San Francisco, California is seeking 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.

Qualifications

• Bachelor’s degree or equivalent in CS or engineering.
• Understanding of deep learning frameworks and system architectures.
• Experience with inference serving for throughput and latency.
• Collaborative across cross-functional teams.
• Bias for action and ability to ship across stacks where opportunity is spotted.
• Strong coding skills and debugging in large codebases.

Responsibilities

• Collaborate with researchers to bring models into production.
• Enable high-performance inference for novel architectures.
• Design tools and architectures improving performance and efficiency.
• Optimize codebase and compute fleets for GPU usage.
• Extend orchestration frameworks for distributed inference and large batches.
• Establish reliability and observability standards across the inference stack.
• Publish learnings via internal docs, open-source libraries, or technical reports.

Skills

Bachelor level CS/Engineering

Deep learning frameworks

Inference serving systems

Cross-functional collaboration

Bias for action

Strong coding and debugging

Education

Bachelor’s degree or equivalent

Tools

PyTorch

JAX

SGLang

vLLM

Kubernetes

Ray

SLURM

Job description

The mission of Thinking Machines is to build AI that extends human will and judgment.

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 $350,000 - $475,000 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.