Software Engineer, Metropolis Vision AI

Developing advanced Metropolis Vision AI pipelines using computer vision and deep learning models for real-time and streaming applications.

Building distributed services to process video, image, and 3D data in both edge and cloud environments.

Enhancing multi-modal perception capabilities to understand complex real-world scenes by combining 2D, 3D, and temporal information.

Utilizing simulation and synthetic data tools for building, testing, and validating perception algorithms at scale.

Optimizing GPU-accelerated inference pipelines to meet specific latency, efficiency, and reliability requirements.

Working with partner teams on technical implementations.

Contributing to technical reviews and guidelines for code quality and testing.

Completed a BS or MS in Computer Science, Electrical Engineering, or a related field, or equivalent experience.

Held a professional software development role for at least 2 years with modern C++ (14/17/20) and Python on Linux.

Possess robust computer science knowledge, including algorithms, data structures, concurrency, and distributed systems concepts.

Have experience in computer vision and deep learning.

Have experience in implementing concurrent systems and Linux-based environments with containers and microservices, integrating AI components into scalable back-end services.

Have practical experience using 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.

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/postprocessing.

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.

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