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.