Senior Software Engineer, Metropolis Vision AI

NVIDIA NVIDIA · Semiconductors · Ho Chi Minh City, Vietnam +1

Senior Software Engineer for NVIDIA's Metropolis Vision AI team, focusing on building and optimizing high-performance, large-scale distributed Vision AI platforms for real-time and streaming scenarios. The role involves implementing and refining pipelines for processing video, image, and 3D data, contributing to multi-modal perception, using synthetic data, and profiling/tuning GPU-accelerated inference. Requires strong C++/Python, Linux, computer vision, deep learning, and production deployment experience.

What you'd actually do

  1. Implementing and optimizing high-performance Metropolis Vision AI pipelines for real-time and streaming scenarios using 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. Contributing to 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
  • computer science fundamentals (algorithms, data structures, concurrency, distributed systems)
  • computer vision
  • deep learning
  • deploying production systems
  • building and debugging high-performance, concurrent systems (multi-threading, asynchronous I/O, memory management)
  • Linux-based environments with containers and microservices
  • integrating AI components into scalable back-end services
  • rapidly prototyping vision models and pipelines
  • PyTorch (training, fine-tuning, deploying models)
  • analytical and problem-solving skills
  • data-driven approach to performance optimization and system build
  • English communication skills

Nice to have

  • end-to-end computer vision applications in production (video analytics, smart cities, autonomous systems, retail analytics, industrial inspection, digital twins)
  • GPU acceleration (CUDA, TensorRT)
  • low-level optimization for inference and pre/post-processing
  • simulation and 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

  • high-performance
  • large-scale distributed
  • real-time
  • streaming
  • production systems
  • GPU-accelerated inference pipelines
  • strict latency, efficiency, and reliability targets
  • modern C++ (14/17/20) and Python on Linux
  • deploying production systems in these fields
  • high-performance, concurrent systems
  • scalable back-end services
  • production-quality services
  • PyTorch in training, fine-tuning, and deploying models for vision tasks
  • performance optimization
  • end-to-end computer vision applications in production
  • GPU acceleration (such as CUDA, TensorRT, or comparable technologies) and low-level optimization for inference and pre/post-processing

Other signals

  • large-scale distributed Vision AI platforms
  • deploying production systems
  • GPU-accelerated inference pipelines
  • real-time and streaming scenarios