Senior Software Engineer, Metropolis Vision AI

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Software Engineer to develop and optimize high-performance Vision AI pipelines and large-scale distributed services for processing video, image, and 3D data. The role involves crafting real-time systems, developing multi-modal perception, using simulation/synthetic data, and profiling/tuning GPU-accelerated inference pipelines. Collaboration with research and platform teams is key, with an emphasis on bringing research into production at scale.

What you'd actually do

  1. Crafting and implementing high-performance Vision AI pipelines for real-time and streaming scenarios using brand-new computer vision and deep learning models.
  2. Developing and refining large-scale distributed services responsible for processing video, image, and 3D data in both edge and cloud settings.
  3. Developing multi-modal perception capabilities that combine 2D, 3D, and temporal information to understand complex real-world scenes.
  4. Using simulation and synthetic data tools to build, test, and validate perception algorithms at scale.
  5. Profiling and tuning GPU-accelerated inference pipelines to meet strict latency, efficiency, and reliability targets.

Skills

Required

  • modern C++ (14/17/20)
  • Python
  • Linux
  • algorithms
  • data structures
  • concurrency
  • distributed systems
  • computer vision
  • deep learning
  • PyTorch
  • high-performance, concurrent systems
  • multi-threading
  • asynchronous I/O
  • efficient memory management
  • Linux-based environments
  • containers
  • microservices
  • scalable back-end services
  • vision models and pipelines
  • analytical and problem-solving skills
  • data-driven approach
  • performance optimization
  • written and verbal communication skills
  • collaboration

Nice to have

  • CUDA
  • TensorRT
  • simulation
  • synthetic data creation
  • Omniverse
  • Unreal Engine
  • Unity
  • vision-language models
  • multimodal AI
  • multimedia
  • video-centric processing
  • codecs
  • video pipelines
  • media frameworks

What the JD emphasized

  • deploying production systems
  • high-performance
  • GPU-accelerated inference
  • low-level optimization

Other signals

  • large-scale distributed services
  • high-performance
  • production systems
  • GPU-accelerated inference