LLM Inference Performance Engineer

We are looking for an LLM Inference Performance Engineer to optimize large-scale LLM inference performance across TPU/GPU and other AI accelerators. You will work across LLM inference, kernels, compilers, and runtime systems, improving latency, throughput, scalability, and overall inference efficiency. Responsibilities - Optimize LLM inference performance on TPU/GPU and other AI accelerators - Develop and optimize inference backends, kernels, and runtime components - Optimize Attention, GEMM, KV Cache, Sampling, fused kernels and other performance-critical workloads - Work with technologies such as JAX, XLA, Pallas, CUDA, Triton or related frameworks - Build benchmarking and profiling tools to identify performance bottlenecks - Collaborate with model, inference, compiler, and hardware teams to improve production performance Requirements - Bachelor’s degree or equivalent experience in CS, Engineering, ML, Systems, or related fields - Experience in LLM inference, ML systems, hardware acceleration, or performance optimization - Strong programming skills in C++ and/or Python - Understanding of GPU/TPU architecture, memory behavior, kernels, or ML workload performance - Experience with performance profiling and benchmarking