We are hiring aDirector, ML Engineeringto own the engineering function behindFireflyFoundrys model services at enterprise scale. This is a multi-faceted executive role with end-to-end accountability for howFireflyFoundrys models are productionized, served, andoperatedfor our enterprise customers and for the engineering organization that delivers that capability.

You will define and implement the technical strategy that turnsFireflyFoundryfrom a managed service for early-design partners into a platform capable of serving hundreds of enterprise customers concurrently each with their own IP, tenancy boundaries, and SLAs powering everything from franchise extensions and new IP development to modern GenAI workflows at production scale. Specifically, you will:

• Set the engineering operating model for productionizing custom generative models across image, video, and 3D including the architectural patterns that simplify pipeline construction and let us absorb a heterogeneous mix of internal and external models without linear engineering cost.
• Own the unit economics ofFireflyFoundryinference. Cost-to-serve, GPUutilization, and gross margin on the managed service are your numbers.
• Define the tenancy and data-isolation architecture that lets us honor enterprise IP contracts under audit.
• Drive the self-serve roadmap that broadensFireflyFoundrys reach and value beyond hands-on engagements.
• Represent Adobe engineering in C-suite and senior technical conversations with studios, brands, and global enterprises including VPs of Production, CTOs, and Chief Digital Officers.

• Applied Scienceto ensure inference quality matches the training environment, and to make prioritization calls on emerging techniques for multimedia
• FireflyFoundryStudioto translate ambitious creative visions into reliable, high-performance ML systems that transform how content is conceived,produced, anddelivered, and into concrete roadmaps with clear milestones and success metrics.
• Post-sales field organizationengagement managers and creative technologists in customer engagements, where you serve as the engineering leadership representative and educate the field on APIs and services.
• AI Platform, and adjacent Adobe orgsto negotiate shared infrastructure, accelerator capacity, and serving primitives at platform scale.
• Strategic partnersincluding GPU vendors andhyperscalers, where capacity planning, roadmap alignment, and partnership economics are part of your remit.

• Lead a multi-team engineering organization of ML engineers and engineering managers; recruit, hire, develop, andretainsenior technical and leadership talent, and build a culture of engineering rigor and delivery discipline.
• Hiring at scale is a material part of this role FireflyFoundryis growing rapidly, and sustaining scaling momentum depends on it. You will integrate top technical and leadership talent into the organization at thepacethe business demands.
• Establish the engineering bar, the bench, and the talent strategy that letFireflyFoundrysustain 10x growth incapabilitybreadth and traffic without linear headcount growth.
• Define the operating rhythm goal-setting, exec reviews, and engineering reviews that keeps a fast-scaling org coordinated.

• Own the multi-year architecture for training and inference at scale: pipeline construction, data pipelines, evaluation frameworks, model lifecycle management, and acceleratorutilization(CUDA, NCCL, and the wider GPU stack).
• Set the strategy for fast model deployment, parallel pipeline operation at scale, tenancy/data isolation, and self-serve capability buildout.
• Make the build-vs-buy and prioritization calls on emerging GenAI techniques in partnership with Applied Science, based on material improvements in capability, cost, or speed.

• Hold the line onproductionSLAs for orchestrated and deployed model services.
• Own analytics and observability across every model pipeline quality, latency, cost, andutilization.
• Drive cost-to-serve down on a multi-year curve while expanding capability.

• Represent engineering in technical customer engagements with enterprise customers translating creative and business requirements into ML roadmaps, milestones, and success metrics.
• Co-design scalable, cost-efficientserving forreal-time on-set use cases and high-volume social content generation, in partnership with infrastructure and platform teams.
• Steward the GPU vendor andhyperscalerrelationships that underwriteFireflyFoundrysserving capacity.

• 10+ yearsin applied machine learning and ML systems, including5+ yearsleading engineering organizations with prior experience leading managers of managers.
• Demonstrated success shipping generative AI products in production at enterprise scale.
• Proven ability tooperateas a peer to VP-level partners across product, science, infrastructure, and field organizations, and torepresentengineering credibly in front of senior customer and partner executives.
• Track recordof building engineering benches, defining career frameworks for emerging roles, and developing leaders who themselves become directors.

• Deep understanding of the modern generative model landscape (diffusion, transformers, VAEs, latent video models, control/adapters, or similar) enough to make architecture, investment, and prioritization calls with confidence, in close partnership with Applied Science.
• Strong intuition for the economics and engineering reality of large-scale inference: accelerator stacks, model optimization and quantization, and the tradeoffs between quality, latency, and cost.
• Experience designing and operating ML systems end-to-end data, training, evaluation, deployment, monitoring, and continuous improvement at production scale.
• Familiarity with high-resolution media pipelines (4K+ video, high bit-depth) or adjacent bandwidth- and latency-sensitive domains is a meaningful plus.

• Executive presence and communication skills able to brief executives, customers, and partners credibly, and to work directly with creative, production, and business collaborators to turn ambiguous problems into clear technical plans.
• Sound judgment under ambiguity comfortable making decisions with incomplete information and revising asnew informationarrives.

• MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience building and leading advanced ML systems.

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