Senior Inference Engineer

Production LLM serving experience: You've deployed and served LLMs using vLLM, SGLang, or TensorRT-LLM, ideally at a company built around inference at scale.

Inference optimization know-how: Hands-on experience with quantization, batching, caching, and routing, not just familiarity with the terms.

Hands-on programming experience: Strong engineering skills in Python or Golang, with real production code experience.

Communication: Strong communication skills, explaining technical concepts clearly to both engineers and non-technical stakeholders.

Familiarity with containerized environments (Docker, Kubernetes)

Hands-on generative AI experience with common ML frameworks (PyTorch, Transformers)

Good understanding of the GPU stack: CUDA, NCCL, drivers, and related libraries

Knowledge of model architectures and fine-tuning approaches

Experience with NVIDIA Dynamo

We are a venture-backed tech startup and the company pioneering Kubernetes virtualization for the AI era. We raised +$30M from top-tier VCs such as Khosla Ventures (first investor in OpenAI, GitLab, Stripe, Doordash) and are in a hyper-growth phase looking for motivated people to complement our team. Our headquarters are in San Francisco (Salesforce Tower), but our team is distributed around the globe and we have a remote-first work culture.

We are the leading platform for operating GPU infrastructure, enabling AI Cloud providers to deliver a hyperscaler-like experience to their customers and AI factories that need to build that same experience for their internal teams. Our platform delivers the full operational stack operators need to run their GPU data centers — managed Kubernetes, fast isolated tenant provisioning, and automated node provisioning and lifecycle management — enabling them to accelerate time to value, reduce operational burden, and maximize the ROI of every GPU.