Director, ML Engineering

Adobe•San Jose, CA

17h•$206,400 - $384,675

About The Position

Firefly Foundry is a new business venture at Adobe, offering enterprise-managed services for custom generative AI in multimedia. This offering includes custom image, video, and 3D models tuned to customer IP, integrated with creative workflows and deployed across Adobe surfaces, supported by a media intelligence layer. The business has seen significant growth in Media & Entertainment, marketing, and retail, and is expanding into new sectors. We are seeking a Director, ML Engineering to lead the engineering efforts for Firefly Foundry’s model services at an enterprise scale. This executive role involves end-to-end responsibility for the production, serving, and operation of Firefly Foundry’s models for enterprise clients, as well as managing the engineering organization that supports these capabilities. The role will define and execute the technical strategy to scale Firefly Foundry from a managed service for early partners to a platform serving hundreds of enterprise customers concurrently, each with unique IP, tenancy, and SLAs. This includes powering franchise extensions, new IP development, and GenAI workflows at production scale.

Requirements

10+ years in applied machine learning and ML systems.
5+ years leading engineering organizations, including experience managing managers.
Demonstrated success shipping generative AI products in production at enterprise scale.
Proven ability to operate as a peer to VP-level partners across product, science, infrastructure, and field organizations.
Ability to represent engineering credibly in front of senior customer and partner executives.
Track record of building engineering benches, defining career frameworks, and developing leaders.
Deep understanding of the modern generative model landscape (diffusion, transformers, VAEs, latent video models, control/adapters, or similar).
Strong intuition for the economics and engineering reality of large-scale inference (accelerator stacks, model optimization and quantization, quality/latency/cost tradeoffs).
Experience designing and operating ML systems end-to-end (data, training, evaluation, deployment, monitoring, continuous improvement) at production scale.
Executive presence and communication skills.
Ability to work directly with creative, production, and business collaborators.
Sound judgment under ambiguity, comfortable making decisions with incomplete information.
MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Familiarity with high-resolution media pipelines (4K+ video, high bit-depth) or adjacent bandwidth- and latency-sensitive domains.

Responsibilities

Define and implement the technical strategy for scaling Firefly Foundry's model services.
Set the engineering operating model for productionizing custom generative models (image, video, 3D), including architectural patterns for pipeline construction and integration of diverse models.
Own the unit economics of Firefly Foundry inference, including cost-to-serve, GPU utilization, and gross margin.
Define the tenancy and data-isolation architecture to ensure compliance with enterprise IP contracts.
Drive the self-serve roadmap to expand Firefly Foundry's reach and value.
Represent Adobe engineering in C-suite and senior technical discussions with enterprise clients.
Build and lead a multi-team engineering organization, focusing on recruitment, development, and retention of senior technical and leadership talent.
Establish the engineering bar, bench, and talent strategy to support significant growth in capability and traffic.
Define the operating rhythm, including goal-setting and review processes, for a fast-scaling organization.
Own the multi-year architecture for large-scale training and inference, covering pipeline construction, data pipelines, evaluation frameworks, model lifecycle management, and accelerator utilization.
Set the strategy for rapid model deployment, parallel pipeline operation, tenancy/data isolation, and self-serve capability development.
Make build-vs-buy and prioritization decisions on emerging GenAI techniques in collaboration with Applied Science.
Own production reliability and economics, ensuring adherence to production SLAs for model services.
Oversee analytics and observability across model pipelines (quality, latency, cost, utilization).
Drive down cost-to-serve while expanding capabilities.
Represent engineering in technical customer engagements, translating business requirements into ML roadmaps.
Co-design scalable and cost-efficient serving solutions for real-time and high-volume content generation.
Steward relationships with GPU vendors and hyperscalers to secure serving capacity.