Manager, Machine Learning Engineering – Video AI & Studio AI

Job Description

What We Do

We build next-generation AI systems for video, spanning both large-scale video understanding and creative studio workflows. Our work combines computer vision, multimodal learning, and machine learning to enable capabilities such as scene understanding, metadata generation, visual effects, storyboarding, and color enhancement across media content.

We focus on taking ML from research to production, integrating models into real-world systems used by engineering, product, and creative teams. Our goal is to build scalable, high-quality solutions that power both content intelligence and creative workflows.

What You’ll Do

• Lead a team of machine learning engineers working on video AI across understanding and creative applications
• Stay hands-on, contributing directly to modeling, prototyping, and production systems
• Drive execution across the full ML lifecycle: problem definition, modeling, evaluation, and deployment
• Partner with product, creative, and engineering teams to define priorities and deliver impactful ML-powered features
• Build and scale production-grade ML systems for video understanding and creative workflows
• Balance rapid iteration with building robust, scalable systems
• Provide technical leadership and mentorship, raising the bar for ML and engineering practices
• Collaborate with platform and infrastructure teams to ensure reliable and scalable solutions

Qualifications & Experience

• 8+ years of experience in machine learning / software engineering, with a focus on computer vision, video understanding, and gen AI
• 2+ years of experience leading or mentoring engineers, with a strong preference for hands-on leadership
• Strong experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
• Proven track record of building and deploying end-to-end ML systems in production
• Strong background in computer vision, video understanding, or multimodal systems
• Experience with large-scale datasets and distributed systems
• Experience with generative AI techniques (e.g., diffusion, image/video generation)
• Strong system design and architecture skills
• Ability to operate in ambiguous, fast-moving environments while maintaining execution focus
• Strong collaboration and communication skills across technical, product, and creative teams

How We Get Things Done…

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.