Deep Learning Applications Engineer

Responsibilities

• 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…

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