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, we are increasingly known as "the AI computing company".
NVIDIA is hiring world class Software Engineers with AI Compiler and distributed systems experience for its Deep Learning Compiler Technology team. The team builds JaxPP, NVIDIA's open-source library for pipeline parallelism in JAX. JaxPP turns a JAX training program into per-stage tasks and runs them across many GPUs. With it, very large models can be trained on NVIDIA GPU clusters using unmodified JAX and XLA. Join the team that is developing advanced compiler and runtime technology for the entire deep learning community.
What you'll be doing:
- Design and implement program transformations on JAX programs (jaxpr) for large-scale training, on top of unmodified JAX and XLA: pipeline schedules, placement of tasks across stages, sharding (including JAX explicit sharding), and overlap of communication with computation.
- Develop technologies and products involving optimizing compilers and runtime execution frameworks for accelerating deep learning training on large scale distributed systems.
- Work on compiling a graph intermediate representation (IR) to target specific code, and build the distributed runtime that dispatches per-stage programs to many GPUs and moves data between stages through communication libraries such as NCCL.
- Contribute to advancing the state of the art in compiler technology for delivering high performance on large-scale training workloads, including mixture-of-experts models.
- Collaborate with members of the software framework teams (for example JAX and XLA) and the hardware architecture teams to accelerate the next generation of deep learning software, and work on integrating JaxPP into JAX training frameworks, including MaxText.
- Be a core contributor to the open-source JaxPP project: keeping it working with new JAX releases, testing, performance tracking, and working with teams who adopt it for their training runs.
What we need to see:
- Masters or PhD in Computer Science, Computer Engineering, or related field or equivalent experience.
- 6+ years of relevant work or research experience in compilers, distributed systems, or deep learning training.
- Compiler design and implementation.
- Distributed system design, development, and deployment.
- Experience with deep learning frameworks (JAX, PyTorch).
- GPU training and inference of deep learning models.
- C/C++ and Python programming, with excellent software design skills, including debugging, performance analysis, and test design.
- Experience with parallel computing libraries (MPI).
- Be able to work independently, define project goals and scope, and lead your own development efforts.
- Strong communication skills are required, including working directly with engineers at partner companies to debug and tune large training runs, along with the ability to work in a dynamic product-oriented team.
Ways to stand out from the crowd:
- Experience with JAX internals (jaxpr, tracing, sharding, shard_map), or with XLA, StableHLO, or MLIR.
- Experience building or tuning large training jobs with pipeline, tensor, expert, and data parallelism, for example in MaxText or Megatron-LM, and debugging their performance on large GPU clusters.
- Knowledge of GPU communication libraries such as NCCL, NVSHMEM, or RDMA, and of CPU and/or GPU architecture. CUDA programming experience is a plus.
- Contributions to open-source compiler or machine learning projects.
- Experience in mentoring early career engineers and interns is a bonus.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 184,000 USD – 287,500 USD.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.#deeplearningSkills Required
- Master’s or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience
- 6+ years of relevant work or research experience in compilers, distributed systems, or deep learning training
- Compiler design and implementation experience
- Distributed system design, development, and deployment experience
- Experience with deep learning frameworks such as JAX or PyTorch
- GPU training and inference of deep learning models
- C/C++ and Python programming skills
- Experience with debugging, performance analysis, and test design
- Experience with parallel computing libraries such as MPI
- Ability to work independently and define project goals and scope
- Strong communication skills and ability to collaborate directly with partner-company engineers
- Experience with JAX internals, XLA, StableHLO, or MLIR
- Experience with large-scale training parallelism and GPU cluster performance tuning
- Knowledge of NCCL, NVSHMEM, RDMA, or CPU/GPU architecture
- CUDA programming experience
- Contributions to open-source compiler or machine learning projects
- Experience mentoring early-career engineers and interns
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.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
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.”
