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

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior DL Software Engineer focused on optimizing and deploying large multimodal models (LLMs/VLMs) for real-time robotic execution in autonomous vehicles. The role involves advanced model compression, quantization, pruning, distillation, and inference optimization techniques for edge deployment on NVIDIA hardware, integrating with C++ production environments.

What you'd actually do

  1. 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.
  2. Implement advanced compression techniques including Quantization (FP4/FP8), pruning, and knowledge distillation to minimize model footprints without compromising safety-critical accuracy.
  3. 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.
  4. Conduct deep, layer-by-layer model profiling to identify compute and memory bottlenecks, driving targeted optimizations for real-time execution.
  5. Leverage the PyTorch ecosystem to extract standardized model graph representations and automate deployment pipelines for TensorRT conversion.

Skills

Required

  • 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.

Nice to have

  • 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 (HIL) testing, and ultra-low latency requirements.

What the JD emphasized

  • real-time robotic execution
  • autonomous vehicles
  • LLM/VLM
  • production deployments
  • safety-critical accuracy
  • real-time execution
  • high-performance C++ production environment
  • training, deploying, or optimizing large-scale DL models in production environments
  • real-time robotic control
  • embodied AI
  • autonomous decision-making
  • ultra-low latency requirements

Other signals

  • optimize LLM/VLM for edge deployment
  • real-time robotic execution
  • low-latency inference