Deep Learning Applications Engineer at NVIDIA – Korea | Jobyssey

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.

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