Staff AI Inference Kernel Engineer

Sail; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Sail builds the world’s most efficient software for inference and agent hosting. In this role, you’ll own token processing down to the lowest layers of the stack, optimize kernel performance, develop new request scheduling and parallelism strategies, and help us use a heterogeneous mix of hardware at max efficiency.

You’ll design and implement exotic parallelism schemes, write custom GPU kernels for regimes like cascade attention, and understand every microsecond of GPU time spent during a

Qualifications

• Strong understanding of core LLM mechanics (KV cache, mixture-of-experts) and deployment stages.
• Interest in MLSys research (speculative decoding, sparse attention).
• Familiarity with tile-based GPU programming or willingness to learn (Triton, CUTLASS).
• Excellent communication and ability to avoid relying on generated prose.

Responsibilities

• Modify and extend state-of-the-art inference engines like vLLM and SGLang.
• Analyze GPU time per forward pass and explain kernel launches.
• Design exotic parallelism schemes for diverse hardware topologies.
• Write custom GPU kernels to optimize regimes like cascade attention.

Skills

LLM basics

GPU programming

System architecture

Communication

Tools

vLLM

SGLang

Triton

CUTLASS

Job description

Sail builds the world’s most efficient software for inference and agent hosting. In this role, you’ll own token processing down to the lowest layers of the stack, optimize kernel performance, develop new request scheduling and parallelism strategies, and help us use a heterogeneous mix of hardware at max efficiency.

You’ll design and implement exotic parallelism schemes, write custom GPU kernels for regimes like cascade attention, and understand every microsecond of GPU time spent during a