Software Engineer - Inference Performance

Job summary

Responsibilities

We're looking for inference performance engineers who want to make the world's most demanding AI workloads run faster and more efficiently. You'll work across the stack, from the inference engine and runtime through scheduling, serving, and routing.

• Implement and productionize cutting-edge inference techniques, working deep in runtime internals. That includes quantization, speculative decoding, KV-cache reuse, chunked…
• Profile and optimize inference end to end, from kernel launch overhead and memory layout up to request scheduling, prefill/decode disaggregation, and cache-aware routing.
• Turn performance into cost savings. Improve tokens per GPU-hour, raise utilization, and give customers and internal teams clear latency/throughput/cost tradeoffs.
• Bring up and tune new model architectures on new hardware quickly, often in the same week they're released.
• Build benchmarking frameworks that measure real-world performance across model architectures, batch sizes, sequence lengths, and hardware configurations.

What they're looking for

• Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field.
• Experience with one or more general-purpose programming languages, such as Python or C++.
• Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching).
• Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM.
• Demonstrated interest and experience in LLMs.

Tech stack

• Spark
• Go
• vLLM
• Python
• C++
• PyTorch
• CUDA
• Triton