Member of Technical Staff - Inference Systems

Hands-on experience with at least one inference framework like llama.cpp, ONNX Runtime, or MLX, going beyond basic usage into internals and modification.

Experience designing and building benchmarking pipelines, including methodology, validation, and reproducibility.

Strong C++ and Python in performance-sensitive contexts.

Solid understanding of inference fundamentals: quantization, decoding strategies, memory layout, and how they interact.

Experience porting models across runtimes and verifying numerical correctness.

Prior work with external partners or clients in a technical validation or evaluation capacity.

Familiarity with edge inference targets and the constraints that come with them.

You've ported LFM2 onto multiple runtimes and platforms, you know the model inside out, and new ports take you a fraction of the time they did at the start.

You've run multiple partner verifications end-to-end and built enough context to spot weak evaluations quickly and push back with evidence.

The benchmark suite covers inference performance and model quality across the platforms we care about, and both internal teams and partners are using it as a reference.