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

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $208,000–$327,750 / yr
Posted: 13 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $223k

This role $268k

$167k most similar roles pay here $345k

This role pays more than 78% of similar roles. Most pay $196,087–$250,812 — the shaded band above. At the midpoint, this role pays about $268k versus about $223k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior AI Architect, Foundation Models and SoC Co-Design, Autonomous Vehicles

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Senior AI Architect, Foundation Models and SoC Co-Design – Autonomous Vehicles joins the team to define next-generation AI model paradigms for autonomous vehicles and shape how these models co-evolve with embedded SoC architectures. The role involves researching and forecasting emerging architectures like Vision-Language-Action and multimodal foundation models while driving hardware-software co-design across GPU, CPU, DLA, memory hierarchy, and interconnects. Responsibilities include analyzing compute, memory, bandwidth, and latency for transformers, diffusion models, or MoE systems to influence silicon design decisions. The candidate will prototype model paradigms on NVIDIA DRIVE platforms, evaluate trade-offs in power efficiency and real-time constraints, and define benchmarking metrics for safety and robustness. Required expertise includes deep learning architecture, distributed training, inference optimization techniques like quantization and pruning, and experience with CUDA, TensorRT, Triton, and TensorRT-LLM within the autonomous vehicle domain.

Skills

AI Machine Learning Deep Learning CUDA TensorRT Triton TensorRT-LLM GPU CPU DLA NVIDIA DRIVE Jetson Diffusion Models MoE Quantization Pruning Distillation

What you'll do

Research and forecast emerging AI model architectures like VLA and multimodal foundation models for autonomous vehicle stacks.
Drive hardware-software co-design between next-generation AI workloads and NVIDIA embedded SoC architectures.
Analyze compute, memory, bandwidth, and latency characteristics of complex architectures like transformers and MoE systems.
Influence future silicon, IP, and system-level design decisions through deep workload characterization and performance analysis.
Prototype and evaluate emerging model paradigms on NVIDIA DRIVE platforms to validate scalability and deployment feasibility.
Evaluate trade-offs between latency, throughput, power efficiency, and safety in production autonomous vehicle systems.
Define benchmarking methodologies and evaluation metrics for next-generation AV AI systems including robustness and edge-case performance.

What we're looking for

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

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