Senior DL Software Engineer, Model Optimization and Edge Deployment - Autonomous Vehicles

About the role

NVIDIA is at the forefront of the AI revolution, specifically in the constantly evolving field of Embodied AI. We are seeking a high-caliber Deep Learning Engineer to bridge the gap between cutting-edge multimodal architectures and real-time robotic execution for autonomous vehicles.

Responsibilities

• Develop SOTA model optimization techniques, such as speculative decoding with block diffusion, KV cache streaming, and Prefill–Decode separation, etc. to boost E2E model performance for production deployments.
• Implement advanced compression techniques including Quantization (FP4/FP8), pruning, and knowledge distillation to minimize model footprints without compromising safety-critical accuracy.
• Design high-performance optimization strategies for inference, including automated model sharding (tensor/sequence parallelism) and the development of efficient attention kernels optimized for KV-caching.
• Conduct deep, layer-by-layer model profiling to identify compute and memory bottlenecks, driving targeted optimizations for real-time execution.
• Leverage the PyTorch ecosystem to extract standardized model graph representations and automate deployment pipelines for TensorRT conversion.
• Scale DL model performance across diverse NVIDIA edge architectures, maximizing the throughput of specialized accelerators on the road.
• Architect the software interface to seamlessly integrate and interact with large-scale models within a high-performance C++ production environment.
• Partner with research, TensorRT, and Cosmos teams to translate breakthrough innovations into shipping product solutions.

Requirements

• PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
• Expert-level proficiency in PyTorch, JAX, or similar machine learning frameworks.
• Sophisticated proficiency with modern LLM/VLM inference stacks, such as vLLM, TensorRT-LLM and SGLang.
• A proven track record of training, deploying, or optimizing large-scale DL models in production environments.
• Deep familiarity with NVIDIA’s deep learning SDKs, specifically TensorRT and CUDA.
• Strong understanding of GPU architecture, the compilation stack, and the ability to debug end-to-end performance across the hardware/software boundary.

Qualifications

• Deep experience with LLM, VLM, and VLA model optimization, specifically tailored for real-time robotic control, embodied AI, and autonomous decision-making.
• Prior experience writing custom high-performance kernels using CUDA, Triton, or CUTLASS to accelerate non-standard neural network layers and specialized attention mechanisms.
• Active contributions to open-source inference and optimization libraries such as vLLM, SGLang and TensorRT-LLM.
• A thorough understanding of the unique constraints of real-time robotics, including safety-critical determinism, hardware-in-the-loop (HIL) testing, and ultra-low latency requirements.

Skills

• Experience with LLM, VLM, and VLA model optimization.
• Proficiency in PyTorch, JAX, or similar machine learning frameworks.
• Familiarity with modern LLM/VLM inference stacks like vLLM, TensorRT-LLM, and SGLang.
• Experience with GPU architecture and CUDA.
• Ability to optimize models for real-time execution and scalability.
• Knowledge of open-source inference and optimization libraries.
• Experience with real-time robotics and safety-critical systems.

Benefits

• Competitive base salary ranging from $184,000 to $287,500 for Level 4, and $224,000 to $356,500 for Level 5.
• Equity and benefits package.

Pay

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.

Schedule

Not specified.

Benefits

• Not specified.

Cookie/Navigation/Equal Opportunity/Scam Warning

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Application Instructions

Applications for this job will be accepted at least until June 9, 2026.