Deep Learning Applications Engineer

NVIDIA is seeking an exceptional AI Engineer – Deep Learning Applications Engineer to create and implement agentic systems and deep learning applications for ADAS solutions. In this role, you will develop innovative workflows, models, and simulations to enhance NVIDIA's driver assistance systems, focusing on LLM/VLM-powered agents and automated bug diagnosis using advanced perception and robotics models integrated with SIL/HIL validation.

Key Responsibilities:

• Design and deploy LLM/VLM-powered agents across the autonomous driving stack, including automated bug diagnosis and triage flows.
• Develop and optimize cutting-edge deep learning models for robotics and ADAS systems.
• Create workflows, models, and simulations to commercialize NVIDIA's driver assistance capabilities.
• Develop agentic workflows for SIL and HIL solutions and integrate them with validation and test infrastructure.
• Collaborate with cross-functional teams to advance features from prototype to SIL/HIL and production readiness.

Requirements:

• BS/MS or higher in Computer Engineering, Computer Science, Electrical Engineering, Robotics, or a related field.
• At least 2 years of professional software engineering experience.
• Proven experience in AI/ML, automation, test infrastructure, or platform/tooling.
• Hands-on experience with automotive-related embedded platforms, including in-vehicle ECU/SoC stacks, ADAS, IVI, AUTOSAR/Linux-based automotive software, and automotive validation/SIL/HIL.
• Proficiency with modern LLM/VLM APIs, prompt engineering, and agent frameworks such as LangChain, AutoGen, or CrewAI.
• Strong Python skills for agent orchestration, tooling, and data pipelines, and working knowledge of C/C++ for reading embedded code and interpreting logs.
• Practical experience with Git, Docker, CI/CD, and test or verification frameworks.
• Excellent analytical and communication skills, with the ability to learn quickly and manage assigned features with guidance.
• Experience with SIL/HIL or simulation tied to ADAS perception, planning, or validation pipelines.

Preferred Qualifications:

• PhD in Robotics and Deep Learning.
• Deep embedded literacy, including schematics, memory maps, RTOS/Linux log parsing, and understanding hardware constraints.
• Experience fine-tuning open-source models like 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 offers highly competitive salaries and a comprehensive benefits package. Explore more at www.nvidiabenefits.com/