Senior Deep Learning Frameworks CUDA Software Engineer

Nvidia

Confirmed live today High trust

Closes in 5 days Remote

Quick summary

Work type: Remote
Location: Santa Clara, CAAustin, TX
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 57 days ago
Freshness: Confirmed live today
Closes: Nov 1, 2026 (soon)
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $211k

This role $236k

$150k $302k

below market most similar roles pay here above market

This role pays more than 78% of similar roles. Most pay $185,663–$235,750 — the blue band above. At the midpoint, this role pays about $236k versus about $211k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Deep Learning Frameworks CUDA Software Engineer

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The Senior Deep Learning Frameworks CUDA Software Engineer joins the team responsible for core CUDA features and runtimes for scaling Deep Learning and HPC applications. This role involves integrating new CUDA features and runtime abstractions into AI frameworks like PyTorch, TRT-LLM, vLLM, SGLang, and JAX. Key responsibilities include performing deep analysis of AI workloads, designing fault-tolerant solutions for large-scale multi-GPU multi-node systems, and developing exploratory tools to profile and accelerate new deep learning paradigms. The position requires proficiency in Python, C++, CUDA, and performance profiler toolchains like NVIDIA Nsight Systems. Candidates must understand computer system architecture, compiler technologies, and communication libraries such as NCCL and MPI to solve complex problems regarding compute and communication overlap in distributed machine learning environments.

Skills

CUDA PyTorch JAX TRT-LLM vLLM SGLang Python C++ Triton XLA NCCL MPI UCX NVIDIA Nsight Systems HPC Compiler Technologies TensorRT Megatron MaxText

What you'll do

Integrate new CUDA features and Runtime abstractions into AI frameworks like PyTorch, TRT-LLM, and JAX.
Perform deep analysis of AI workloads to identify innovation opportunities in the lower layers of the stack.
Drive improvements in the AI Compiler-Runtime interface to build multi-GPU and multi-node solutions.
Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
Develop exploratory tools and runtime systems to profile and accelerate new deep learning paradigms.
Write maintainable code to transition exploratory prototypes into open-source releases or commercial products.
Influence the core CUDA roadmap to facilitate the development of next-generation deep learning frameworks.

What we're looking for

BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field (or equivalent experience).
8+ years of relevant industry experience or equivalent academic experience after completing a degree.
Development experience with Deep Learning Frameworks such as PyTorch and JAX, and Inference Engines like TRT-LLM, vLLM, or SGLang.
Rapid prototyping and development skills using Python, C++, CUDA, or related DSLs.
Solid grasp of AI models, parallelisms, and compiler technologies like torch.compile.
Experience conducting performance benchmarking on AI clusters and familiarity with profiler toolchains like NVIDIA Nsight Systems.
Understanding of HPC/AI communication concepts and computer system architecture, HW-SW interactions, and operating systems principles.
Deep expertise in performance internals of major DL frameworks and hands-on experience with CUDA, NCCL, and distributed machine learning (preferred).

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