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

Responsibilities

• Develop state‑of‑the‑art model optimization techniques—speculative decoding with block diffusion, KV cache streaming, Prefill–Decode separation—to boost end‑to‑end 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, such as automated model sharding (tensor/sequence parallelism) and 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.

• 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.

• Deep experience with LLM, VLM, and VLA model optimization, specifically tailored for real‑time robotic control, embodied AI, and autonomous decision‑making.
• Proven track record of implementing low‑bit inference.
• 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.
• Thorough understanding of the unique constraints of real‑time robotics, including safety‑critical determinism, hardware‑in‑the‑loop testing, and ultra‑low latency requirements.

• Base salary range: USD 184,000 – 287,500 for Level 4; USD 224,000 – 356,500 for Level 5.
• Eligibility for equity and additional benefits.

NVIDIA is committed to fostering a diverse work environment and is a proud equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.