NVIDIA is looking for an outstanding AI Engineer – Deep Learning Applications Engineer to design and build agentic systems and deep learning applications that deliver ADAS solutions. In this position, you will develop creative workflows, models, and simulations to productize NVIDIA driver assistance systems—from LLM/VLM-powered agents and automated bug diagnosis through innovative perception and robotics models integrated with SIL/HIL validation. This role requires hands-on experience on automotive-related embedded platforms, solid AI/ML and agent engineering skills, and the ability to give end-to-end from prototype to SIL/HIL and test infrastructure. A real passion for applying agentic AI and deep learning to production ADAS, and creativity in solving complex autonomous-driving and embedded problems, will be fully applied in this role.
What you'll be doing:
Design and deploy LLM/VLM-powered agents for use cases across the autonomous driving stack, including automated bug diagnosis and triaging flows.
Develop and optimize innovative deep learning models for robotics and ADAS systems.
Build workflows, models, and simulations to productize NVIDIA driver assistance capabilities.
Develop agentic workflows for SIL and HIL solutions and integrate them with validation and test infrastructure.
Collaborate with solutions architecture, validation, firmware, and customer-facing teams to deliver features from prototype through SIL/HIL toward production readiness.
What we need to see:
BS/MS or higher in Computer Engineering, Computer Science, Electrical Engineering, Robotics, or a related field (or equivalent experience).
2+ years of relevant professional software engineering experience.
Demonstrated work in AI/ML, automation, test infrastructure, or platform/tooling
Hands-on experience on embedded systems in automotive-related platforms (e.g., in-vehicle ECU/SoC stacks, ADAS, IVI, AUTOSAR/Linux-based automotive software, automotive validation/SIL/HIL, or OEM/Tier-1 environments). Ability to read embedded logs, understand hardware–software constraints, and collaborate with firmware and validation engineers.
Solid proficiency with modern LLM/VLM APIs, prompt engineering, and agent frameworks (e.g., LangChain, AutoGen, CrewAI, or custom orchestration).
Strong proficiency in Python (agent orchestration, tooling, data pipelines) and working proficiency in C/C++ to read embedded code, interpret logs, and collaborate with firmware/validation teams.
Practical experience with Git, Docker, CI/CD, and test or verification frameworks used for automated software validation.
Strong analytical and communication skills; ability to learn quickly and own assigned features with guidance from senior engineers and multi-functional partners.
Hands-on SIL/HIL or simulation experience tied to ADAS perception, planning, or validation pipelines.
Ways to stand out from the crowd:
PhD in Robotics and Deep learning is prefered
Deep embedded literacy: schematics, memory maps, RTOS/Linux log parsing, and hardware constraints beyond typical platform bring-up.
Experience fine-tuning open-source models (e.g., Llama-3, Mistral, Qwen) with LoRA/QLoRA for perception, code generation, or log analysis.
Background in automated software verification, fuzzing, or symbolic execution.
Publications, open-source contributions, or shipped projects in robotics, ADAS, or agentic automation.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you consider your future, explore what we provide for you and your family at www.nvidiabenefits.com/
Skills Required
- BS/MS or higher in Computer Engineering, Computer Science, Electrical Engineering, Robotics, or related field (or equivalent experience)
- 2+ years of relevant professional software engineering experience
- Demonstrated work in AI/ML, automation, test infrastructure, or platform/tooling
- Hands-on experience on embedded systems in automotive-related platforms (in-vehicle ECU/SoC stacks, ADAS, IVI, AUTOSAR/Linux-based automotive software, automotive validation/SIL/HIL, OEM/Tier-1 environments)
- Proficiency with modern LLM/VLM APIs, prompt engineering, and agent frameworks (e.g., LangChain, AutoGen, CrewAI, or custom orchestration)
- Strong proficiency in Python for agent orchestration, tooling, and data pipelines
- Working proficiency in C/C++ to read embedded code, interpret logs, and collaborate with firmware/validation teams
- Practical experience with Git, Docker, CI/CD, and automated test/verification frameworks
- Hands-on SIL/HIL or simulation experience tied to ADAS perception, planning, or validation pipelines
- Strong analytical and communication skills; ability to learn quickly and own assigned features
- PhD in Robotics and Deep Learning
- Deep embedded literacy: schematics, memory maps, RTOS/Linux log parsing, hardware constraints
- Experience fine-tuning open-source models (e.g., Llama-3, Mistral, Qwen) with LoRA/QLoRA
- Background in automated software verification, fuzzing, or symbolic execution
- Publications, open-source contributions, or shipped projects in robotics, ADAS, or agentic automation
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
