Senior Software Engineer, Quantized Inference

We are now looking for a Senior Software Engineer for Quantized Inference! NVIDIA is seeking software engineers to accelerate the discovery and deployment of efficient inference recipes for LLMs. A recipe defines which operators are transformed into low‑precision or sparsified variants — unlocking throughput and latency gains without regressing accuracy or verbosity. Recipes may incorporate techniques such as rotations, block scaling to attenuate outlier impact, or improved calibration data drawn from SFT/RL pipelines.

Each new recipe demands corresponding kernel and model‑level implementations in inference engines (vLLM, TRT-LLM, SGLang). The candidate will translate recipe specifications into functionally correct, performant code, e.g., writing Triton kernels, inserting quantize/dequantize nodes into prefill and decode paths, and ensuring per‑expert scaling in MoE layers is handled correctly. From there, the candidate will collaborate with partner inference teams to further optimize throughput and interactivity on target workloads.

This work is a core component of our productization effort across Megatron‑LM, Model Opt, and vLLM.

• Implement quantized and sparse recipes in inference engines (vLLM, TRT‑LLM, SGLang)
• Own model export pipelines (Model Opt, Megatron‑LM Hugging Face), ensuring quantized checkpoints serialize correctly for downstream serving
• Build prototypes and benchmarking harnesses to evaluate recipe throughput/interactivity before full optimization
• Develop data analysis tooling and visualizations for numerics debugging
• Improve developer productivity across the team: CI, build systems, training infrastructure, pipeline friction
• Participate in code reviews and incorporate feedback

• Proficient in Python; familiarity with C++
• Strong software engineering fundamentals: concise, well‑tested code; fluent with AI‑assisted tooling
• Experience with ML accelerators with a basic understanding of how certain ML layers affect execution time