Senior Software Engineer - GPU Local AI Platforms

Posted 12 Days Ago
Be an Early Applicant
5 Locations
In-Office
224K-431K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.
Summary Generated by Built In

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA's Local AI team is building the software stack that makes large language models and generative AI applications run at maximum efficiency on NVIDIA edge AI hardware. The AI ecosystem moves fast; our job is to make sure end users get the best experience. We own the platform — performance, CI/CD pipelines, validated recipes, and model bring-up infrastructure — that lets developers run groundbreaking LLMs out of the box. The open-source community builds fast; our platform is what turns community innovation into something developers and partners can rely on at scale.

What you'll be doing:

  • Track and evaluate innovations in leading open-source LLM inference frameworks — identify performance-critical features and algorithmic improvements relevant to NVIDIA edge AI hardware

  • Analyze how new model architectures and inference algorithms (attention variants, MoE routing, speculative decoding, multi-token prediction, quantized inference) map onto NVIDIA GPU architecture — identify mismatch, fallback paths, and optimization opportunities

  • Characterize multi-node inference behavior: collective communication primitives (NCCL/RCCL), topology-aware all-reduce strategies, and parallelism efficiency on edge cluster configurations

  • Produce performance analysis reports mapping theoretical hardware limits (memory bandwidth, FLOP/s, interconnect throughput) to observed inference throughput, latency, and utilization

  • Own the model validation workflow for new model releases: architecture compatibility assessment, inference recipe development, performance characterization, and publication to developer recipe sites

  • Develop and maintain developer-facing inference recipes: keep them accurate as frameworks evolve, automate staleness detection, and build feedback loops from CI results to recipe updates

  • Engage with community and partners on model bring-up questions; serve as the technical point of contact for hardware-specific inference issues related to partner concerns

What we need to see:

  • BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.

  • 12+ years of software engineering with depth in GPU computing, ML systems, or high-performance inference

  • Strong Python or C++ programming, software design, and software engineering skills.

  • Hands-on experience with GPU kernel development or optimization (CUDA/C++, Triton, or equivalent) — you understand how thread blocks, memory hierarchy, and warp execution affect real-world performance

  • Working knowledge of LLM inference internals: attention mechanisms, KV-cache management, continuous batching, quantization formats, and tensor parallelism

  • Container engineering expertise: multi-architecture Docker or OCI builds, layer optimization, runtime configuration, NVIDIA Container Toolkit

  • Strong analytical skills: ability to form a performance hypothesis, design an experiment, interpret results, and communicate findings clearly

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ 

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 28, 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.

Skills Required

  • BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience
  • 12+ years software engineering experience with depth in GPU computing, ML systems, or high-performance inference
  • Strong Python or C++ programming, software design, and software engineering skills
  • Hands-on experience with GPU kernel development or optimization (CUDA/C++, Triton, or equivalent)
  • Working knowledge of LLM inference internals: attention mechanisms, KV-cache management, continuous batching, quantization formats, tensor parallelism
  • Container engineering expertise: multi-architecture Docker or OCI builds, layer optimization, runtime configuration, NVIDIA Container Toolkit
  • Strong analytical skills: design performance experiments, interpret results, and communicate findings

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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