Advancing the state of the art in Video AI for real-world perception systems at Cisco
Requirements
- Bachelor’s, Master’s, or PhD degree in Artificial Intelligence or a closely related field (e.g., Computer Vision, Machine Learning, Robotics, Computer Science)
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with video pipelines, multimodal learning, or sensor fusion
- Understanding of model deployment considerations such as latency, memory, robustness, and scalability
- Experience with 3D perception techniques such as depth estimation, multi-view geometry, point clouds, SLAM, or neural rendering
- Publications, patents, or open-source contributions demonstrating applied research impact
- Strong hands-on experience with AI model research and training
Responsibilities
- Research and develop advanced methods for video perception, with a strong emphasis on both 2D and 3D understanding
- Investigate approaches for tasks such as detection, tracking, scene understanding, reconstruction, and multimodal perception
- Define data collection, curation, and annotation strategies to support effective training and evaluation of Video AI models
- Train, evaluate, and refine models through systematic experimentation and quantitative analysis
- Develop prototypes, reference implementations, and tooling to validate research ideas and guide downstream implementation
- Partner closely with production engineering teams to transition research models into deployed systems, advising on architecture, performance trade-offs, and integration considerations
- Stay current with advances in computer vision and applied machine learning research, and assess their applicability to real-world problems
Other
- Bachelor’s degree plus 3 years of relevant experience in AI model research and training
- Master’s degree plus 1 year of relevant experience in AI model research and training
- PhD degree with no additional industry experience required
- Minimum of 5 years of hands-on experience in AI model research and training
- Collaboration across research, software, and product teams to deliver ML-driven capabilities
