Senior Manager, System Software Engineering - Metropolis Accelerated And Inferencing Software

NVIDIA Engineering Leader

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 standards of living across the globe.

NVIDIA is looking for an engineering leader who is hands-on with deep learning—comfortable reading/modeling code, not just running it. You bring strong intuition for modern architectures (e.g., transformers, diffusion, VLMs etc), deep experience tuning for NVIDIA GPUs (kernels, memory, latency/efficiency trade-offs) / SOCs, and a proven record 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 to experts in the industry is a great fit for this role!

What You'll Be Doing

• Lead, encourage, and develop world-class engineering teams distributed across various India locations.
• 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.
• 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 build support. Apply customer insights to influence 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.
• Representing Nvidia Deep learning solutions in webinars, conferences and partner events

What We Need to See

• Masters in Computer Science/Electrical Engineering or equivalent experience.
• A minimum of 8 years of meaningful involvement in machine learning/deep learning research or practical experience, coupled with 6+ years of leadership background and overall 12+ 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.
• Deep knowledge of GPU, CPU and dedicated deep learning architecture fundamentals and low-level performance optimizations using heterogeneous computing.
• Hands-on experience with Multimedia Frameworks, Computer Vision, VLMs, LLMs, or multimodal AI systems applied to perception, data triage, or automated labeling.
• Strong expertise in large-scale data processing, systems build, or machine learning pipelines.
• Strong communication, careful planning, and technical leadership capabilities.

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 Smart Spaces, Physical AI with a deep understanding of constraints and advancements of sensing, computing, and model architecture evolutions.
• Ability to lead and drive global teams across multiple continents and time zones
• Deep experience with CV, LLMs, VLMs, GenAI Models, and standards.

With a competitive salary package and benefits, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. Are you a creative and autonomous director-level engineer who loves challenges? Do you have a genuine passion for advancing the state of Data Science across a variety of industries? If so, we want to hear from you!