Senior Compiler Engineer - AI
NVIDIA is hiring a Senior Compiler Engineer - AI in Santa Clara, United States. It pays $184k-$288k a year and Level rates it ; you can apply on NVIDIA.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, JAX and Xla.
- Level classifies it as a senior role.
- Level lists 2,700+ other open roles at NVIDIA.
- The role was posted within the past week.
AI in this role
Design and implement advanced deep learning compilers and distributed runtime technologies for large-scale GPU training.
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.
Applications for this job will be accepted at least until November 6, 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.#deeplearningHow we rate this
Senior Compiler Engineer - AI at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where compiler design was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where gpu computing was part of your work. What did you do?
- Tell me about a project where c was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Compiler Design, Deep learning, Distributed Systems, GPU Computing, and C. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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