Impress employers and recruiters.
Choose from hundreds of resume examples.

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

We are seeking an infrastructure research engineer to design, optimize, and scale systems for large AI models, aiming to make inference faster, more cost-effective, reliable, and reproducible. This role supports research and real-world applications by ensuring smooth, large-scale operation of experiments, evaluations, and deployments.

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 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.

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.
• 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.

Skills

• Deep learning frameworks (e.g., PyTorch, JAX)
• Inference serving systems optimized for throughput and latency (e.g., SGLang, vLLM)
• Distributed compute systems
• GPU parallelism
• Hardware-aware optimizations
• ML or systems infrastructure projects (e.g., SGLang, vLLM, PyTorch, Triton, DeepSpeed, XLA)

Location

• San Francisco, California

Work Type

• Onsite

Experience Level

• Mid-level
• Senior

Education Level

• Bachelor's degree or equivalent experience

Salary/Compensations

• $350,000 - $475,000 USD

Benefits

• Generous health, dental, and vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support as needed
• Visa sponsorship

About the Company

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