NVIDIA's technology is at the heart of the AI revolution, touching people across the planet by powering everything from self-driving cars, robotics, co-pilots, and more. Join us at the forefront of technological advancement in intelligent assistants and information retrieval. Metropolis is transforming how the physical world is perceived and understood using advanced computer vision and deep learning. Our team builds large-scale distributed Vision AI platforms that power intelligent spaces, smart cities, retail analytics, and digital twins. This role offers the opportunity to contribute to core components of a strategic platform with high visibility and real-world impact. As a System Software Engineer for Vision AI, you will develop and optimize high-performance vision systems that turn massive streams of video, image, and 3D data into actionable insights. You will collaborate with specialists in perception, simulation, and large models to bring research into production at scale.
What you’ll be doing:
Implementing high-performance Metropolis Vision AI pipelines for real-time and streaming scenarios using computer vision and deep learning models.
Developing large-scale distributed services responsible for processing video, image, and 3D data in both edge and cloud settings.
Assisting to multi-modal perception capabilities that combine 2D, 3D, and temporal information to understand complex real-world scenes.
Using simulation and synthetic data tools to build, test, and validate perception algorithms at scale.
Profiling GPU-accelerated inference pipelines to meet strict latency, efficiency, and reliability targets.
Collaborating with partner teams to implement technical builds.
Participating in technical reviews and contributing to guidelines for code quality and testing.
What we need to see:
BS or MS in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
2+ years of professional software development experience using modern C++ (14/17/20) and Python on Linux.
Strong computer science fundamentals, including algorithms, data structures, concurrency, and distributed systems concepts.
Experience in computer vision and deep learning.
Experience in implementing concurrent systems, including multi-threading, asynchronous I/O, and efficient memory management.
Experience in Linux-based environments with containers and microservices, integrating AI components into scalable back-end services..
Practical experience with PyTorch in training, fine-tuning, and deploying models for vision tasks.
Strong analytical and problem-solving skills, with a data-driven approach to performance optimization and system build.
Excellent written and verbal English communication skills, with demonstrated success collaborating across time zones and functions.
Ways to stand out from the crowd:
Practical experience implementing end-to-end computer vision applications in production, such as video analytics, smart cities, autonomous systems, retail analytics, industrial inspection, or digital twins.
Practical experience with low-level optimization for inference and pre/post-processing.
Experience in simulation and synthetic data creation employing tools such as Omniverse, Unreal Engine, Unity, or similar digital-twin platforms..
Background in multimedia, including video-centric processing and delivery (such as codecs, video pipelines, or media frameworks) and integrating vision models into multimedia workflows.
Skills Required
- BS or MS in Computer Science, Electrical Engineering, or related field, or equivalent experience
- 2+ years professional software development experience using modern C++ (14/17/20) and Python on Linux
- Strong computer science fundamentals including algorithms, data structures, concurrency, and distributed systems concepts
- Experience in computer vision and deep learning
- Experience implementing concurrent systems (multi-threading, asynchronous I/O, efficient memory management)
- Experience in Linux-based environments with containers and microservices, integrating AI components into scalable back-end services
- Practical experience with PyTorch in training, fine-tuning, and deploying models for vision tasks
- Strong analytical and problem-solving skills with a data-driven approach to performance optimization
- Excellent written and verbal English communication skills and cross-timezone collaboration experience
- Practical experience implementing end-to-end computer vision applications in production (video analytics, smart cities, autonomous systems, retail analytics, industrial inspection, digital twins)
- Practical experience with low-level optimization for inference and pre/post-processing
- Experience in simulation and synthetic data creation using Omniverse, Unreal Engine, Unity, or similar
- Background in multimedia, including video-centric processing and delivery (codecs, video pipelines, media frameworks)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
