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Principal Machine Learning Engineer

Job in San Jose, Santa Clara County, California, 95199, USA
Listing for: Adobe
Full Time position
Listed on 2026-09-12
Job specializations:
  • Software Development
    Software Architect, AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Backend Developer
Salary/Wage Range or Industry Benchmark: 261800 - 379100 USD Yearly USD 261800.00 379100.00 YEAR
Job Description & How to Apply Below

The Opportunity Firefly Foundry is Adobe's enterprise managed-service offering for custom multimedia generative AI — deep-tuned image, video, and 3D models built on each customer's 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 architected, optimized, 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 Adobe's 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 composing, optimizing, 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 integrates first-party and third-party generative models into Adobe's flagship products.
  • Technical direction across multiple engineering teams as the principal authority on architecture and design — a cross-team scope, distinct from the Director's org-wide roadmap and management ownership.
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 leadership — to align technical 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 to align on shared 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 Adobe's flagship products.
  • Architect ML serving workflows for enterprise-scale model customization, deployment, and ecosystem integration — including externalizable, self-serve fine-tuning flows.
  • Co-develop and optimize GPU-accelerated inference pipelines — prioritizing latency, throughput, scalability, and reliability — using tools such as PyTorch, CUDA, Triton, and TensorRT.
  • Design and architect the product backend and plugin ecosystem that lets internal applications and external integrations consume Firefly Foundry's 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 and MLOps technologies — serving runtimes, quantization, GPU scheduling — to improve engineering velocity and…
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