Senior Integration Engineer, End-to-End Model, Autonomous Vehicles

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Employment: Full-time
Posted: 9 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $194k

This role $197k

$141k most similar roles pay here $252k

This role pays more than 53% of similar roles. Most pay $152,955–$235,750 — the shaded band above. At the midpoint, this role pays about $197k versus about $194k for comparable roles.

Based on 240 similar postings.

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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 892 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 870 roles with salary data.

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At a glance

TL;DR · Senior Integration Engineer, End-to-End Model, Autonomous Vehicles

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As a Senior Integration Engineer, End-to-End Model - Autonomous Vehicles, you will join the team developing end-to-end autonomous driving applications to transform how vehicles perceive and act in complex environments. You will accelerate the development, integration, evaluation, and deployment of driving models across large-scale training infrastructure, simulation environments, and production vehicle platforms. Your daily responsibilities include integrating learned models with sensor inputs, localization, mapping, and safety systems while establishing runtime interface contracts and optimizing inference for latency, throughput, and power requirements. You will also develop tools to evaluate driving quality and perform in-vehicle testing. The role requires expertise in C++, Python, CUDA, TensorRT, and PyTorch on Linux or QNX systems. This position focuses on the technical challenge of turning rapidly evolving AI models into reliable, high-performance autonomous driving functionality on heterogeneous computing platforms.

Skills

C++ Python CUDA TensorRT PyTorch Linux QNX Machine Learning Model Inference Quantization Real-time Operating Systems Autonomous Driving ADAS Robotics performance-analysis Software Architecture

What you'll do

Integrate learned driving models with vehicle interfaces, sensor inputs, localization, and safety systems.
Establish clear contracts for model input, output, timing, state-management, and runtime interfaces.
Partner with developers to improve model quality, debuggability, and readiness for production deployment.
Investigate and resolve discrepancies between model behavior in development environments and on target vehicle platforms.
Optimize model inference and software to meet latency, throughput, memory, determinism, and power requirements.
Develop tools and metrics for evaluating driving quality, safety, robustness, and regression performance at scale.
Perform in-vehicle testing, collect and analyze driving data, and complete autonomous driving missions.
Develop high-quality production code in C++ and Python using CUDA and GPU-accelerated technologies.

What we're looking for

PhD with 1+ year, MS with 3+ years, or BS with 5+ years of experience in CS, CE, Robotics, ML, or a related field.
Strong C++ programming, software architecture, debugging, and performance analysis skills.
Proficiency in Python and experience with modern machine learning frameworks like PyTorch.
Experience with model inference technologies such as CUDA and TensorRT.
Experience developing software on Linux and embedded or real-time operating systems like QNX.
Experience integrating machine learning models into complex, performance-sensitive production systems.
Experience with autonomous driving, robotics, ADAS, or other real-time intelligent systems.
Experience deploying end-to-end driving, robotics, or embodied-AI models on production hardware (preferred).

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