Applied Scientist developing machine learning models for computer vision in film production. Enhancing AI-driven rendering and perception systems for seamless workflows.
Responsibilities
Develop and enhance rendering, perception and compositing systems
Design, fine-tune and implement advanced face detection, clustering, and compositing algorithms for DE long form pipelines.
Optimize and scale training and inference systems for high-volume video processing.
Translate research breakthroughs into robust, efficient production code.
Conduct rapid experiments on state-of-the-art architectures (Transformers, diffusion models, 3DGS) to improve robustness and accuracy.
Evaluate, benchmark, and document model performance against production metrics.
Identify and resolve bottlenecks in existing AI workflows.
Partner with Science, Engineering, and Product team members to ensure seamless integration of AI modules.
Mentor other scientists, engineers and Product folk and contribute to the technical roadmap for character prep & vubbing group.
Advocate for scalable, production-ready solutions that directly enhance user experience.
Requirements
Strong experience developing and applying computer vision or machine learning methods for visual understanding problems (e.g., face perception, motion/pose estimation, or video analysis).
OR
Experience developing neural rendering or 3D generation systems (e.g., novel view synthesis, scene reconstruction, or generative models for photorealistic rendering).
A strong product mindset — motivated by building systems that deliver tangible value to users, not just technical novelty.
Comfortable working at both the algorithmic and implementation levels, from model design and optimization to large-scale data processing and integration in production systems.
Proficiency in Python, with a strong foundation in computer science and problem-solving.
Expertise with deep learning frameworks (PyTorch) and vision tools (OpenCV).
MSc/PhD in Visual Computing, Computer Science, or a related field, or equivalent professional experience.
Hands-on experience with deep learning approaches such as Vision Transformers, GANs, diffusion models, VAEs, or emerging methods (e.g., 3DGS).
Bonus points for:
Experience developing multi-modal systems that integrate audio, text, and visual inputs.
Experience working with cross-functional product and engineering teams.
Benefits
Autonomy
A hybrid working environment
Competitive Salary
All permanent employees receive generous stock options
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