Job description

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, ModelOpt, and vLLM. What you'll be doing: • Implement quantized and sparse recipes in inference engines (vLLM, TRT-LLM, SGLang) • Own model export pipelines (ModelOpt, Megatron-LM HuggingFace), 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 What we need to see: • 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 • Familiarity with PyTorch internals (custom ops, autograd, export) or equivalent framework • Experience reading, modifying, or contributing to a large open-source codebase • MS/PhD in Computer Science or related field, or equivalent experience. • 4+ years in a relevant software engineering role • Demonstrated ability to move fast with ambiguous requirements, with strong written and verbal communication Ways to stand out from the crowd: • Experience contributing to inference serving frameworks (vLLM, TRT-LLM, SGLang) or Triton kernel development • Track record of debugging numerical issues across mixed-precision boundaries • Deep experience with model compression techniques: PTQ, QAT, structured/unstructured sparsity Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 24, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Implement quantized and sparse recipes in inference engines (vLLM, TRT-LLM, SGLang)
Own model export pipelines (ModelOpt, Megatron-LM HuggingFace), 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
Familiarity with PyTorch internals (custom ops, autograd, export) or equivalent framework
Experience reading, modifying, or contributing to a large open-source codebase
MS/PhD in Computer Science or related field, or equivalent experience.
4+ years in a relevant software engineering role
Demonstrated ability to move fast with ambiguous requirements, with strong written and verbal communication
Experience contributing to inference serving frameworks (vLLM, TRT-LLM, SGLang) or Triton kernel development
Track record of debugging numerical issues across mixed-precision boundaries
Deep experience with model compression techniques: PTQ, QAT, structured/unstructured sparsity
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 24, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmpythonaiml

Platform: eightfold
Effort: medium