Job description

NVIDIA is a world leader in physical AI, powering self-driving cars, humanoid robots, intelligent environments, medical devices, and more. Our software platforms are at the core of this mission, enabling innovators to build world-changing products that save lives, improve working conditions, and elevate living standards across the globe. This team is the execution engine behind NVIDIA’s Vision AI strategy—owning the full lifecycle from model onboarding to production deployment. We transform foundation models into real-time, GPU-accelerated video intelligence systems using DeepStream and VSS. Our focus is on scaling multimodal reasoning, enabling agentic development workflows, and closing the loop between production data and model improvement—positioning NVIDIA as the default platform for Physical AI. NVIDIA is looking for a Director of Systems Engineering, who is hands-on with deep learning and comfortable reading/modeling code, not just running it. You bring strong intuition for modern architectures (e.g., transformers, diffusion, and VLMs); deep experience tuning on NVIDIA GPUs (kernels, memory, and latency/efficiency trade-offs)/SOCs; and a proven record of delivering robust, low-latency inference at scale. You have led teams that turn Accelerated Computing pipelines into reliable, measurable business impact for embedded and Enterprise platforms. You will work with a cohesive, high-performing team that’s been built and refined over the past nine years. An individual well-aligned with industry experts is a great fit for this role! What You'll be Doing: • Lead, encourage, and develop world-class engineering and data teams decentralized across Europe, Asia, and the United States. • Architect and operationalize NVIDIA’s end-to-end data Inference Acceleration strategy, powering inference and continuous performance improvements. • Drive strategic implementations of TensorRT, VLLM, and other accelerated frameworks for inference solutions for Edge and Enterprise devices: Lead Accelerated Computing efforts and solutions for key Metropolis verticals. Set up Proofs of Readiness (PORs) and guide their implementations. • Collaborate with major Metropolis OEMs and Partners to architect highly accelerated and optimized custom deep learning models and inference pipelines for their specific requirements. • Offer direct customer support, including debugging, technical education, and handling customer inquiries for our Metropolis partner and customers. Responsible for drafting and finalizing SOWs with internal customers and partners. • Performance Benchmarking: Orchestrate efforts to achieve leading performance results on industry benchmarks like MLPerf on various edge and Enterprise devices. • Technical Leadership & Influence: Function as a technical leader for deep learning across multiple teams, giving oversight and building support. Apply customer insights to shape the composition and structure of upcoming SOC/GPU deep-learning hardware. • Scaling the Team: Strategically hiring to meet new demands while also mentoring and adjusting existing teams to new deep learning challenges. • Represent Nvidia Deep Learning solutions in webinars, conferences, and partner events What We Need to See: • Bachelor’s and/or Master's in Computer Science/Electrical Engineering or equivalent experience. • 15+ years of overall experience, with a minimum of 10+ years of meaningful involvement in machine learning/deep learning research or practical experience, coupled with 7+ years of leadership background. • Over 10 years of validated industry expertise in the embedded software sector, holding technical leadership positions accountable for delivering outstanding production software within a multifaceted setting. • Deep knowledge of GPU, CPU, and dedicated deep learning architecture fundamentals, and low-level performance optimizations using heterogeneous computing. • Hands-on experience with VLMs, LLMs, or multimodal AI systems applied to perception, data triage, or automated labeling. • Strong expertise in large-scale data processing, systems building, or machine learning pipelines. • Strong communication, careful planning, and technical leadership capabilities. Ways to Stand Out from the Crowd: • PhD in a relevant field such as Spatial Computing & Awareness, Sim-to-Real Transfer, Human-to-Physical AI Interaction, • Deep experience with CV, LLMs, VLMs, GenAI models, and standards. • Technical thought leadership in production deployment of Smart Spaces, Physical AI, with a deep understanding of constraints and advancements of sensing, computing, and model architecture evolutions. • Current experience leading and driving global teams across multiple continents and time zones With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and versatile people in the world working with us, and our engineering teams are growing fast in some of the most impactful fields of our generation: Physical AI, Smart Spaces, and Vision AI. If you're a creative engineer who enjoys autonomy and shares our passion for technology, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until July 26, 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.

Requirements

Must-haves

Lead, encourage, and develop world-class engineering and data teams decentralized across Europe, Asia, and the United States.
Architect and operationalize NVIDIA’s end-to-end data Inference Acceleration strategy, powering inference and continuous performance improvements.
Collaborate with major Metropolis OEMs and Partners to architect highly accelerated and optimized custom deep learning models and inference pipelines for their specific requirements.
Performance Benchmarking: Orchestrate efforts to achieve leading performance results on industry benchmarks like MLPerf on various edge and Enterprise devices.
Scaling the Team: Strategically hiring to meet new demands while also mentoring and adjusting existing teams to new deep learning challenges.
Represent Nvidia Deep Learning solutions in webinars, conferences, and partner events
Bachelor’s and/or Master's in Computer Science/Electrical Engineering or equivalent experience.
15+ years of overall experience, with a minimum of 10+ years of meaningful involvement in machine learning/deep learning research or practical experience, coupled with 7+ years of leadership background.
Over 10 years of validated industry expertise in the embedded software sector, holding technical leadership positions accountable for delivering outstanding production software within a multifaceted setting.
Deep knowledge of GPU, CPU, and dedicated deep learning architecture fundamentals, and low-level performance optimizations using heterogeneous computing.
Hands-on experience with VLMs, LLMs, or multimodal AI systems applied to perception, data triage, or automated labeling.
Strong expertise in large-scale data processing, systems building, or machine learning pipelines.
Strong communication, careful planning, and technical leadership capabilities.
PhD in a relevant field such as Spatial Computing & Awareness, Sim-to-Real Transfer, Human-to-Physical AI Interaction,
Deep experience with CV, LLMs, VLMs, GenAI models, and standards.
Technical thought leadership in production deployment of Smart Spaces, Physical AI, with a deep understanding of constraints and advancements of sensing, computing, and model architecture evolutions.
Current experience leading and driving global teams across multiple continents and time zones
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until July 26, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

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Keywords to mirror

aimachine learning

Platform: eightfold
Effort: medium

Benefits

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Vision