JAX is the framework of choice for the most ambitious AI research, and the datacenters it runs on are evolving fast. NVIDIA supercomputers are becoming more heterogeneous combining GPUs, CPUs, and LPUs, and JAX needs to evolve to perform well across all of them. NVIDIA seeks a Senior Engineering Manager to define and drive NVIDIA's JAX strategy, coordinating multiple teams and workstreams to ensure JAX delivers peak performance across the platform, from single-accelerator workloads to hundreds of thousands of devices on the largest supercomputers ever built.
A critical part of this role is ensuring JAX is ready on next-generation architectures, including Rubin Ultra, LPUs, and the CPUs that drive them, from day one. Doing that well requires keeping up with where AI is heading. This means supporting emerging needs across training, post-training, inference, and robotics, bridging new hardware capabilities with AI trends. Come join us to build the team and technology behind the next AI breakthroughs.
What you will be doing:
Drive the engineering contribution strategy across the JAX ecosystem stack — including JAX core, XLA, Shardy, MPMD, and application-level codebases like MaxText — prioritizing where NVIDIA's contributions create maximum impact on performance, adoption, and ecosystem influence
Promote teamwork across organizational boundaries to shape NVIDIA's AI platform across compiler (XLA, TileIR, CUDA), runtime, networking, and hardware teams
Build deep partnerships with key open-source projects, especially Google's JAX team — supporting our partners while representing NVIDIA's interests in shaping the project's direction
Design processes and define clear success criteria to keep teams aligned to outcomes
Build staffing plans, review team capacity, and make strategic hiring decisions to stay ahead of current and future needs
Lead, grow, and mentor a high-performing engineering organization — developing technical leaders and creating space for your team to experiment, prototype, and build innovative systems that improve products and processes
What we need to see:
MS or PhD in Computer Science, Computer Engineering, or a related field (or equivalent experience)
10+ overall years of software engineering experience, with 4+ years in engineering management or technical leadership
Strong technical background in system software, compilers, high-performance computing, or AI frameworks
Proven experience leading domain-level strategy and projects that span organizational boundaries, building consensus, influencing without direct authority, and tailoring communication to the audience from engineers to executives
Track record of owning outcomes across multiple projects and teams, balancing trade-offs, and driving progress with incomplete information
Experience identifying development cycle gaps and defining workflows across teams and organizations to improve efficiency
Experience leading teams that contribute to large-scale projects with external collaborators
Ways to stand out from the crowd:
Hands-on experience with JAX and its systems like XLA, HLO, PJRT, Shardy, or JAX's tracing and transformation system
Experience leading teams working on compiler or runtime infrastructure for deep learning frameworks (JAX, PyTorch, TensorFlow or equivalent)
Familiarity with NVIDIA's AI platform components: GPU programming, CUDA, cuDNN, NCCL, TensorRT, Transformer Engine and heterogeneous compute targets (GPU, CPU, LPU)
Experience driving performance optimization efforts for large-scale distributed AI training, post-training (RLHF, alignment), inference, or robotics workloads (multi-node, multi-GPU)
Experience driving framework or software bring-up on new hardware architectures, coordinating across silicon, driver, compiler, and framework teams
Skills Required
- MS or PhD in Computer Science, Computer Engineering, or related field (or equivalent experience)
- 10+ years overall software engineering experience
- 4+ years in engineering management or technical leadership
- Strong technical background in system software, compilers, high-performance computing, or AI frameworks
- Proven experience leading domain-level strategy and cross-organizational projects, building consensus and influencing without authority
- Track record of owning outcomes across multiple projects and teams, balancing trade-offs and driving progress with incomplete information
- Experience identifying development cycle gaps and defining workflows across teams to improve efficiency
- Experience leading teams that contribute to large-scale projects with external collaborators
- Hands-on experience with JAX and systems like XLA, HLO, PJRT, Shardy, or JAX tracing and transformation
- Experience leading teams working on compiler or runtime infrastructure for deep learning frameworks (JAX, PyTorch, TensorFlow)
- Familiarity with NVIDIA AI platform components: GPU programming, CUDA, cuDNN, NCCL, TensorRT, Transformer Engine, heterogeneous compute
- Experience driving performance optimization for large-scale distributed AI training, inference, or robotics workloads
- Experience driving framework or software bring-up on new hardware architectures across silicon, driver, compiler, and framework teams
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 — 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.
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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.
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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
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.”
