Senior Software Engineer, Metropolis Vision AI

Details

NVIDIA's technology is at the heart of the AI revolution, powering applications from self-driving cars and robotics to co-pilots and more. Metropolis is transforming how the physical world is perceived and understood using advanced computer vision and deep learning. The team builds large-scale distributed Vision AI platforms that power intelligent spaces, smart cities, retail analytics, and digital twins. This role focuses on developing and optimizing high-performance vision systems that turn massive streams of video, image, and 3D data into actionable insights and bringing research into production at scale.

Responsibilities

• Craft and implement high-performance Vision AI pipelines for real-time and streaming scenarios using modern computer vision and deep learning models.
• Develop and refine large-scale distributed services responsible for processing video, image, and 3D data in both edge and cloud settings.
• Build multi-modal perception capabilities combining 2D, 3D, and temporal information to understand complex real-world scenes.
• Use simulation and synthetic data tools to build, test, and validate perception algorithms at scale.
• Profile and tune GPU-accelerated inference pipelines to meet strict latency, efficiency, and reliability targets.
• Collaborate with partner teams across product, research, and platform to translate requirements into clear technical builds and robust implementations.
• Drive technical build reviews, promote guidelines for code quality and testing, and mentor other engineers on Vision AI systems development.

Requirements

• BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or a related field, or equivalent experience.
• 12+ years of professional software development experience using modern C++ (C++14/17/20) and Python on Linux.
• Strong computer science fundamentals, including algorithms, data structures, concurrency, and distributed systems concepts.
• Demonstrated expertise in computer vision and deep learning, with experience deploying production systems in these fields.
• Experience building and debugging high-performance, concurrent systems, including multi-threading, asynchronous I/O, and efficient memory management.
• Proficiency working in Linux-based environments with containers and microservices, integrating AI components into scalable back-end services.
• Ability to rapidly prototype vision models and pipelines and evolve them into production-quality services.
• Practical experience with PyTorch for training, fine-tuning, and deploying vision models.
• Strong analytical and problem-solving skills, with a data-driven approach to performance optimization and system build.
• Excellent written and verbal communication skills, with demonstrated success collaborating across time zones and functions.

Ways to stand out

• Proven experience delivering end-to-end computer vision applications in production (video analytics, smart cities, autonomous systems, retail analytics, industrial inspection, digital twins).
• Practical experience with GPU acceleration technologies such as CUDA and TensorRT and low-level optimization for inference and pre/post-processing.
• Experience in simulation and synthetic data creation using tools such as Omniverse, Unreal Engine, Unity, or similar digital-twin platforms.
• Background in vision-language or multi-modal AI and integrating such models into real products.
• Background in multimedia and video-centric processing and delivery (codecs, video pipelines, media frameworks) and integrating vision models into multimedia workflows.

Benefits

• Competitive salaries and a generous benefits package. You will also be eligible for equity and benefits.

Additional information

• Base salary range: 224,000 USD - 356,500 USD (determined based on location, experience, and pay of employees in similar positions).
• Applications accepted at least until July 12, 2026.
• NVIDIA uses AI tools in its recruiting processes.
• NVIDIA is an equal opportunity employer and is committed to fostering an inclusive work environment.