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The role involves designing, developing, testing, and optimizing software for NVIDIA's SoCs, focusing on performance enhancements and addressing architectural bottlenecks. Responsibilities include competitive analysis and driving performance optimizations across software products.

As a Senior Compiler Engineer at NVIDIA, you will enhance a proprietary GPU compiler backend, optimize register allocation, collaborate with teams on improvements, and contribute to deep-learning compiler technology.

The Principal Chromium and CEF Engineer will work on custom CEF and Chromium code, focusing on improving performance, reliability, and security, while contributing to CI/CD and collaborating with the team on quality indicators.

Design and implement systems for verifying correctness in deep learning compilers, collaborating with architecture teams to analyze optimizations and engineer testing systems for deep learning solutions.

Lead the strategy and evolution of NVIDIA's data platform ecosystem, managing large-scale data infrastructure programs, defining technical requirements, and ensuring effective cross-team collaboration to deliver reliable, scalable systems.

Design and develop real-time applications for desktop and mobile platforms, ensuring reliability and scalability. Collaborate with teams to enhance user experience and maintain code quality.

Lead technical engagement for autonomous aerial systems in defense, integrating NVIDIA software and collaborating with industry leaders to optimize flight-ready solutions.

Lead efforts in developing and enhancing PTX Compiler infrastructure for Generative AI and Deep Learning applications in NVIDIA GPUs. Collaborate with architecture teams to support AI workloads and ensure high-quality code generation.

NVIDIA seeks a Senior Software Engineer to enhance its AI platform using JAX, focusing on performance optimization in deep learning frameworks while collaborating with applied researchers. The role requires strong programming, system design skills, and a proven ability to develop customer-facing solutions.

Collaborate with AI/ML researchers to identify and fix infrastructure and application inefficiencies on GPU clusters; build tools and ML-based analyzers; monitor fleet utilization; optimize hardware, software, and distributed training/inference performance; and promote adoption of efficient AI/ML practices.