Director, ML Services Engineering

What you'll do

• Lead and scale a multi-team engineering organization of ML engineers and managers.
• Define the technical strategy for productionizing custom generative models at enterprise scale.
• Own the unit economics of inference, including cost-to-serve, GPU utilization, and gross margins.
• Design the tenancy and data-isolation architecture to ensure enterprise IP security.
• Manage the multi-year architecture for training, inference, and model lifecycle management.
• Represent engineering in technical and strategic conversations with C-suite executives and global brands.
• Monitor production reliability, SLAs, and observability across all model pipelines.
• Steward relationships with GPU vendors and hyperscalers to secure serving capacity.

What we're looking for

• 10+ years of experience in applied machine learning and ML systems.
• 5+ years of experience leading engineering organizations, including experience leading managers of managers.
• Demonstrated success shipping generative AI products in production at enterprise scale.
• MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
• Deep understanding of the modern generative model landscape including diffusion, transformers, VAEs, and latent video models.
• Experience designing and operating end-to-end ML systems including data, training, evaluation, deployment, and monitoring at production scale.
• Strong intuition for large-scale inference economics, including accelerator stacks, model optimization, and quantization.
• Familiarity with high-resolution media pipelines or adjacent bandwidth- and latency-sensitive domains (preferred).