Inferact is a startup founded by creators and core maintainers of vLLM, the most popular open-source LLM inference engine. Our mission is to grow vLLM as the world. Backed by Sequoia and Redpoint.
About the role
We're looking for a TPU performance engineer to make vLLM a first-class inference engine on Google TPUs. You'll build and optimize TPU backends, compiler integrations, runtime paths, and benchmarking infrastructure using JAX, XLA, Pallas, and related tooling so vLLM can deliver frontier inference performance on TPU hardware.
What they're looking for
- Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar
- Hands-on experience building or optimizing TPU workloads using JAX, XLA, Pallas, or related compiler and runtime tooling
- Deep understanding of TPU execution, memory behavior, compilation, and performance constraints for ML workloads
- Experience optimizing ML kernels or inference paths such as attention, GEMM, sampling, KV cache, fused kernels, or backend runtime paths
- Strong performance profiling and benchmarking skills, with the ability to use measurements, compiler artifacts, correctness tests, and reproducible benchmarks to guide optimization work
- Experience with vLLM, SGLang, TensorRT-LLM, XLA-based serving, or other LLM inference systems
More about this role
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.
We're looking for a TPU performance engineer to make vLLM a first-class inference engine on Google TPUs. You'll build and optimize TPU backends, compiler integrations, runtime paths, and benchmarking infrastructure using JAX, XLA, Pallas, and related tooling so vLLM can deliver frontier inference performance on TPU hardware.
You'll work at the boundary of inference systems, kernels, compilers, and hardware architecture, improving production-relevant model serving on TPU with clear correctness, latency, and throughput benchmarks. Your work will help make TPU support in vLLM usable, fast, benchmarked, and maintainable.
Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.
Hands-on experience building or optimizing TPU workloads using JAX, XLA, Pallas, or related compiler and runtime tooling.
Deep understanding of TPU execution, memory behavior,...
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