Director Deep Learning Solutions jobs at Nvidia in United States, San Jose

What you'll be doing:

• Drive Strategic Implementations of TensorRT inference solutions for Edge devices: Lead TensorRT releases and solutions for key verticals, including Game consoles, Robotics and Autonomous Vehicles for Jetson, DRIVE and GPU + x86 hardware platforms. Set up Proofs of Readiness (PORs) and guide their implementations.

• Coordinate the development and release of Torch-TRT and other alternative optimization frameworks like MLIR-TRT.

• Leading customer solutions: Collaborate with major automotive and robotics OEMs and Partners to adjust and optimize custom deep learning models for their specific requirements. Offer direct customer support, including debugging, technical education, and handling customer inquiries for our Automotive and Robotics partners. Responsible for drafting, negotiating, and finalizing SOWs with customers and partners.

• Performance Benchmarking: Orchestrate efforts to achieve leading performance results on industry benchmarks like MLPerf on various edge devices.

• Technical Leadership & Influence: Function as a technical leader for deep learning across multiple teams, giving oversight and build support. Apply customer insights to influence the composition and structure of upcoming SOC deep learning hardware.

• Scaling the team: Strategically hiring to meet new demands while also mentoring and adjusting existing teams to new deep learning challenges.

• Representing Nvidia Deep learning solutions in webinars, conferences and partner events

What we need to see:

• PhD or equivalent experience in Computer Science/Electrical Engineering

• A minimum of 8 years of meaningful involvement in machine learning/deep learning research or practical experience, coupled with 8+ years of leadership background and overall 15+ years of industry experience.

• Over 10 years of validated expertise in the embedded software sector, holding technical leadership positions accountable for delivering outstanding production software within a multifaceted setting.

• Solid understanding of embedded operating system internals (QNX/Linux), memory management, C/C++, and embedded/system software concepts

• Background in parallel programming, e.g., CUDA, OpenMP

• Deep Knowledge of GPU. CPU and dedicated deep learning architecture fundamentals and low-level performance optimizations using heterogeneous computing.

Ways to Stand Out from the Crowd:

• We welcome candidates with a PhD or equivalent experience in a relevant field

• Leadership role in production deployment of Autonomous solutions for passenger cars, with deep understanding of constraints and advancements of sensing, computing, and model architecture evolutions.

• Lead teams that are located in various regions around the world.

• Experience with Automotive safety standards.