Senior Manager, Software Engineering - JAX

Lead the strategy and execution for NVIDIA’s JAX initiative, overseeing cross-team efforts to optimize performance across single accelerators to hundreds of thousands of devices. You will coordinate multiple teams and external partners to drive JAX, XLA, and related stack improvements on next-generation architectures. You’ll define processes, staffing, and success criteria to keep workstreams aligned with platform-wide goals. This role offers a chance to shape AI tooling that influences large-scale research and production workloads, with a strong focus on performance, portability, and ecosystem leadership.

• Define and drive the engineering contribution strategy across the JAX ecosystem stack (JAX core, XLA, Shardy, MPMD, MaxText) for performance and adoption impact
• Foster cross-organizational collaboration to align NVIDIA's AI platform across compiler, runtime, networking, and hardware teams
• Forge deep partnerships with open-source projects, notably Google's JAX team, while steering project direction
• Design processes and success criteria to maintain alignment to outcomes
• Build staffing plans, assess capacity, and guide strategic hiring
• Lead, mentor, and grow an engineering organization enabling experimentation and rapid prototyping

• MS or PhD in CS/CE or related field (or equivalent experience)
• 10+ years of software engineering experience, including 4+ years in engineering management or technical leadership
• Strong background in system software, compilers, HPC, or AI frameworks
• Proven track record leading cross-boundary strategy and projects, influencing without authority
• Experience identifying development cycle gaps and defining workflows to improve efficiency
• Experience leading teams on large-scale projects with external collaborators

• Strategic thinking and prioritization
• Cross-functional leadership and consensus-building
• Effective communication across engineers and executives
• Experience with JAX and systems like XLA, HLO, PJRT, Shardy, tracing and transformation systems
• Compiler or runtime infrastructure for deep learning frameworks (JAX, PyTorch, TensorFlow)
• Familiarity with NVIDIA AI platform components: CUDA, cuDNN, NCCL, TensorRT, Transformer Engine, heterogeneous compute targets (GPU, CPU, LPU)