Senior AI Architect, Foundation Models and SoC Co-Design – Autonomous Vehicles

• Research and forecast emerging AI model architectures that are expected to shape the future autonomous vehicle stack, including Vision-Language-Action (VLA) models, Multimodal foundation models and more.
• Drive hardware-software co-design across next-generation AI workloads and NVIDIA embedded SoCs, including GPU, CPU, DLA, memory hierarchy, interconnects, and accelerator subsystems.
• Analyze compute, memory, bandwidth, and latency characteristics of sophisticated AI architectures such as transformers, diffusion models, or MoE systems
• Develop architectural insights and influence future NVIDIA silicon, IP, and system-level design decisions through deep workload characterization and performance analysis.
• Prototype and evaluate emerging model paradigms on NVIDIA DRIVE and embedded AI platforms to validate scalability, efficiency, and deployment feasibility.
• Partner closely with AI research, autonomous driving software, compiler, runtime, and hardware architecture teams to align long-term roadmap and platform strategy.
• Evaluate tradeoffs across latency, throughput, power efficiency, safety, and real-time constraints in production AV systems.
• Define benchmarking methodologies and evaluation metrics for next-generation AV AI systems, including robustness, safety, calibration, and edge-case performance.
• MS, PhD, or equivalent experience in Computer Science, Electrical Engineering, Machine Learning, Robotics, or related field.
• 12+ years of experience in AI/ML systems, deep learning architecture, or hardware/software co-design.
• Deep expertise in modern AI architectures and large-scale model systems
• Experience mapping AI workloads onto heterogeneous compute architectures including GPUs, CPUs, NPUs/DLAs, DSPs, and memory subsystems.
• Solid understanding of distributed training systems, scaling laws, and inference optimization techniques.
• Experience with model optimization methods such as quantization, sparsity, pruning, distillation, and memory-efficient inference.
• Understanding of performance profiling, systems bottleneck analysis, and workload characterization.
• Experience with autonomous vehicle or robotics stacks including perception, planning, prediction, or control.
• Deep familiarity with NVIDIA platforms such as DRIVE™, Jetson™, CUDA®, TensorRT™, Triton, or TensorRT-LLM.
• Experience influencing silicon architecture or collaborating directly with hardware design teams.
• Expertise in sophisticated AI efficiency techniques (e.g. FP8/FP4 inference, Mixture-of-Experts routing, Streaming attention and KV-cache optimization)
• Strong understanding of multimodal fusion across camera, lidar, radar, HD maps, and language inputs.
• #AutonomousVehicles
• You will also be eligible for equity and benefits .
• Applications for this job will be accepted at least until September 2, 2026.
• This posting is for an existing vacancy.

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