Principal Machine Learning Engineer

The Opportunity

Firefly Foundry is Adobes enterprise managed-service offering for custom multimedia generative AI deep-tuned image video and 3D models built on each customers IP paired with creative production workflows and a media-intelligence layer and deployed across new and existing Adobe surfaces and products including Firefly Photoshop Illustrator Express Stock and Premiere.

We are hiring a Principal Machine Learning Engineer to serve as the technical lead for our GenAI Services area. This is not a model-training or research role it is the senior-most hands-on engineering authority over how our generative models are architectedoptimized and served at enterprise scale. You will set the inference architecture and technical standards that a growing organization of engineers builds against co-develop and optimize the inference code that makes those systems fast and cost-efficient and architect the APIs and product backend that let Adobes first-party and third-party models reach both internal applications and external plugin integrations. Where the Director owns the multi-year technical strategy headcount and company roadmap for the org you own the architecture technical depth and hands-on execution that make that strategy real spanning multiple engineering teams without owning their people management.

What this role owns

• The technical architecture for composingoptimizing and serving heterogeneous generative model pipelines LLMs diffusion and transformer-based image/video models RAG and retrieval systems multi-turn agentic flows and 3D/mesh pipelines across the GenAI Services area.

• The optimization strategy for inference performance: latency throughput and cost-to-serve across model families and GPU fleets.

• The system design standards for pipeline composition multi-tenant serving and the product backend/API and plugin surface that integratesfirst-partyand third-party generative models into Adobes flagship products.

• Technical direction across multiple engineering teams as the principal authority on architecture and design a cross-team scope distinct from the Directors org-wide roadmap andmanagementownership.

Who you will partner with

• Applied Science to translate research models and emerging techniques into production-grade inference architecture.

• Director ML Engineering and ML Engineering leadershipto aligntechnical architecture with organizational strategy and priorities.

• Product Managers and TPMs to define and deliver against the roadmap for GenAI services and APIs.

• Firefly Foundry Studio and AI Platform to translate creative production workflows into performant services and toalign onshared infrastructure and serving primitives.

What you will do

• Lead the development of core GenAI services and APIs that integrate a wide range of first-party and third-party generative models into Adobes flagship products.

• Architect MLservingworkflows for enterprise-scale model customization deployment and ecosystem integration including externalizable self-serve fine-tuning flows.

• Co-develop andoptimizeGPU-accelerated inference pipelines prioritizing latency throughput scalability and reliability using tools such asPyTorch CUDA Triton andTensorRT.

• Design andarchitectthe product backend and plugin ecosystem that lets internal applications and external integrations consume Firefly Foundrys model services.

• Provide hands-on technical leadership: guide engineers through architecture design implementation and best practices and mentor a growing organization of ML engineers.

• Research and evaluate emerging inference andMLOpstechnologies serving runtimes quantization GPUscheduling to improve engineering velocity and system performance.

• Lead design reviews and set technical standards ensuring high reliability and maintainability across systems.

• Drive cross-functional alignment with Product Managers TPMs and engineering leaders to define and deliver on the roadmap.

• Foster a culture of technical excellence and continuous improvement across the organization.

What you bring

• MS or PhD in Computer Science Machine Learning or a related field or equivalent industry experience.

• 8 years of experience in machine learning engineering including production-scale deployment and serving not training or research experimentation.

• 3 years leading the technical direction of large-scale GPU-intensive GenAI inference systems serving architecture and optimization.

• Deep experience with inference frameworks and tools such asPyTorch CUDA TritonTensorRT Nvidia Dynamo and Python.

• Strong understanding of generative model architectures diffusion models transformers GANs LLMs sufficient to make architecture and optimization calls and reasonaboutoutput quality in partnership with Applied Science.

• Proven experience architecting multi-model pipelines and serving them behind APIs at enterprise scale.

• Experience designing product backend systems and plugin architectures consumed by internal applications and external integrations.

• Proven success leading cross-functional teams through complex high-stakes technical initiatives witha track recordof driving alignment in matrixed organizations.

• Excellent communication and technical leadership skills.

Preferred Qualifications

• Experience with model serving orchestration and GPU resource management in large-scale environments.

• Hands-onexpertisein Kubernetes distributed systems andMLOpsplatforms.

• Experience with RAG architectures and multi-turn agentic conversational systems.

• Experience with quantization distillation or other model-optimization techniques for inference.

Education

• Masters or PhD in Computer Science Electrical Engineering or a related field or equivalent practical experience building and leading production-scale ML systems.

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