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

Job Title

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

Role Summary

Define next-generation AI model paradigms for autonomous vehicles and lead hardware–software co-design with embedded SoC architects. Collaborate with AI researchers, silicon architects, and platform teams to characterize workloads, influence platform roadmaps, and validate deployment feasibility.

Applications accepted until June 5, 2026.

Experience Level

Senior — 12+ years of experience in AI/ML systems, deep learning architecture, or hardware/software co-design.

Responsibilities

Key responsibilities include workload research, co-design, characterization, prototyping, and cross-team collaboration to inform platform and silicon decisions.

• Research and forecast emerging AI model architectures relevant to autonomous vehicles (e.g., vision-language-action, multimodal foundation models).
• Drive hardware–software co-design across embedded SoC subsystems including GPU, CPU, DLA, memory hierarchy, interconnects, and accelerators.
• Analyze compute, memory, bandwidth, and latency characteristics of architectures such as transformers, diffusion models, and MoE systems.
• Develop architectural insights and influence silicon, IP, and system-level design through workload characterization and performance analysis.
• Prototype and evaluate model paradigms on DRIVE and embedded AI platforms to validate scalability, efficiency, and deployment feasibility.
• Partner with research, software, compiler, runtime, and hardware 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, including robustness, safety, calibration, and edge-case performance.

Requirements

Must-have technical skills and experience. Education requirements are summarized separately below.

• Deep expertise in modern AI architectures and large-scale model systems.
• Experience mapping AI workloads to heterogeneous compute architectures (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.
• Proven ability in performance profiling, systems bottleneck analysis, and workload characterization.

• Experience with autonomous vehicle or robotics stacks (perception, planning, prediction, control).
• Familiarity with NVIDIA platforms and frameworks (DRIVE, Jetson, CUDA, TensorRT, Triton).
• Experience influencing silicon architecture or collaborating with hardware design teams.
• Expertise in advanced efficiency techniques (FP8/FP4 inference, Mixture-of-Experts routing, streaming attention, KV-cache optimization).
• Knowledge of multimodal fusion across camera, lidar, radar, HD maps, and language inputs.

Education Requirements

MS or PhD in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related field, or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.