Director, ML Services Engineering

What you'll do

• Build and lead a multi-team engineering organization of ML engineers and managers while scaling the team's headcount and culture.
• Define and implement the technical strategy for training, inference, and model lifecycle management at enterprise scale.
• Manage the unit economics of inference, including GPU utilization, cost-to-serve, and gross margins for managed services.
• Design architecture for multi-tenant data isolation to ensure customer IP protection and compliance under audit.
• Represent engineering leadership in high-level technical discussions with C-suite executives, studio partners, and global brands.
• Collaborate with Applied Science teams to prioritize emerging GenAI techniques based on performance, cost, and speed.
• Manage relationships with GPU vendors and hyperscalers to secure capacity and align on infrastructure roadmaps.
• Translate complex customer requirements into actionable ML roadmaps, milestones, and success metrics for the engineering team.

What we're looking for

• Hold at least 10 years of experience in applied machine learning and ML systems.
• Possess at least 5 years of experience leading engineering organizations, including managing managers.
• Demonstrate success shipping generative AI products in production at enterprise scale.
• Maintain a deep understanding of the modern generative model landscape, including diffusion, transformers, and VAEs.
• Exhibit strong knowledge of large-scale inference economics, including accelerator stacks and model optimization.
• Experience designing and operating end-to-end ML systems for data, training, evaluation, and deployment is required.
• Possess executive presence to engage with C-suite leaders and high-level technical partners.
• Hold an MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.