Software Engineer
Computer Vision Team
Confirmed live in the last 24 hours
Genius Sports

1,001-5,000 employees

Sports data and technology
Remote in USA
Experience Level
Desired Skills
Computer Networking
Computer Vision
Data Structures & Algorithms
Operating Systems
AI & Machine Learning
Software Engineering
  • 2+ years of industrial experience in the full development life cycle: design, prototype, implementation, testing, and performance evaluation of software
  • 2+ years of industrial experience in computer vision and modern machine learning algorithms, such as deep learning
  • BSc or MSc in computer science or a related degree, with strong software engineering and modern computer vision components
  • Solid experience in software engineering: modern software development practices and tools, concurrent and distributed programming, operating systems, computer networks, database systems
  • Fully proficient in Python and modern C++ with exposure to functional & object-oriented programming paradigms. Other languages such as Rust will be considered as an advantage for some of our multidisciplinary projects
  • Experience with high-performance computing, GPU computing, and real-time systems would set you apart
  • Design, prototype, implement and test software and computer vision & machine learning algorithms in Rust, Python, and C++
  • Develop and optimize real-time and high-accuracy sports solutions with modern CV: object detection, recognition and tracking, camera calibration, 3D reconstruction, etc
  • Manage interdisciplinary projects in collaboration with different groups within the company
  • Implement and provide best-practices for maintainable software development, including deployment process, documentation, and adherence to and improvement of coding standards
  • Leverage Amazon Web Services (EC2 and S3) to run algorithms on a large number of servers in the cloud
  • Support and monitor live systems, including on-call rotation for computer vision systems during sports seasons
  • Continuously learn new applications and apply learnings to new challenges