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What you'll do:
- Optimize inference systems for latency and throughput, including batching and quantization.
- Write and tune kernels using CUDA and Triton; deploy heterogeneous clusters.
What we're looking for:
- 1-6 years in systems/infrastructure engineering, strong new grads considered.
- Familiarity with CUDA, Triton, or GPU programming and cluster operations.
Compensation & location:
- $200k-$300k annually, visa sponsorship available.
- Remote within the US; SF as primary location.
Undisclosed
They are an early-stage AI infrastructure startup making large language model inference faster and more cost-efficient. The small team focuses on low-level performance engineering and AI-assisted workflows, with a strong emphasis on technical fundamentals.