Role at a glance
- Salary
- $184K – $287.5K/yr
- Location
- Santa Clara, California, United States
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- 6+ years (academic/ industry) experience with ML/DL systems development preferable
- Education
- Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); PhD are preferred
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Role Summary
This role develops AI systems software for efficient inference on NVIDIA hardware. The team builds libraries, code generators, GPU kernels, abstractions, compilers, and runtimes that accelerate large language models, agents, and other AI workloads, while collaborating across deep learning frameworks, libraries, kernels, and GPU architecture teams.
What You'll Do
- Innovating and developing new AI systems technologies for efficient inference
- Designing, implementing, and optimizing kernels for high impact AI workloads
- Designing and implementing extensible abstractions for LLM serving engines
- Building efficient just-in-time domain specific compilers and runtimes
- Collaborating closely with engineers across deep learning frameworks, libraries, kernels, and GPU architecture teams
- Contributing to open source communities like FlashInfer, vLLM, and SGLang
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View full postingQualifications
Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); 6+ years of ML/DL systems development experience; strong deep learning framework experience; strong Python and C/C++ programming skills.
Required
- Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience)
- 6+ years (academic/ industry) experience with ML/DL systems development
- Strong experience in developing or using deep learning frameworks such as PyTorch, JAX, TensorFlow, or ONNX
- Strong Python and C/C++ programming skills
Preferred
- PhD
- Experience with inference engines and runtimes such as vLLM, SGLang, and MLC
- Background in domain specific compiler and library solutions for LLM inference and training
- Expertise in inference engines like vLLM and SGLang
- Expertise in machine learning compilers such as Apache TVM and MLIR
- Strong experience in GPU kernel development and performance optimizations using CUDA C/C++, cuTile, Triton, or similar
- Open source project ownership or contributions
Original job description
Content provided by the employer
Original job description
Content provided by the employer
We're looking for outstanding AI systems engineers to develop groundbreaking technologies in the inference systems software stack! We build innovative AI systems software to accelerate for AI inference. As a member of the team, you'll develop libraries, code generators, and GPU kernel technologies for NVIDIA's hardware architecture. This means designing and building things like new abstractions, efficient attention kernel implementations, new LLM inference runtimes components, and kernel code generators to accelerate large language models, agents, and other high-impact AI workloads.
What you'll be doing:
Innovating and developing new AI systems technologies for efficient inference
Designing, implementing, and optimizing kernels for high impact AI workloads
Designing and implementing extensible abstractions for LLM serving engines
Building efficient just-in-time domain specific compilers and runtimes
Collaborating closely with other engineers at NVIDIA across deep learning frameworks, libraries, kernels, and GPU arch teams
Contributing to open source communities like FlashInfer, vLLM, and SGLang
What we need to see:
Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); PhD are preferred
6+ years (academic/ industry) experience with ML/DL systems development preferable
Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) and ideally inference engines and runtimes such as vLLM, SGLang, and MLC.
Strong Python and C/C++ programming skills
Ways to stand out from the crowd:
Background in domain specific compiler and library solutions for LLM inference and training (e.g. FlashInfer, Flash Attention)
Expertise in inference engines like vLLM and SGLang
Expertise in machine learning compilers (e.g. Apache TVM, MLIR)
Strong experience in GPU kernel development and performance optimizations (especially using CUDA C/C++, cuTile, Triton, or similar)
Open source project ownership or contributions
You will also be eligible for equity and benefits.
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.About the company
NVIDIA
Large Enterprise
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.