Sr Staff Machine Learning Engineer, Adobe Firefly Services

Adobe Adobe · Enterprise · San Jose, CA +1

Senior Staff Machine Learning Engineer at Adobe focused on building and optimizing scalable, high-performance generative AI services and inference pipelines for integration into Adobe products. The role involves designing GenAI services, APIs, and ML workflows for model customization and serving, with a strong emphasis on GPU-accelerated training and inference optimization.

What you'd actually do

  1. Design and Development of core GenAI services and APIs that integrate a wide range of generative models into Adobe’s flagship products.
  2. Design and build ML workflows for enterprise-scale model customization, serving, and ecosystem integration.
  3. Collaborate with Adobe Research and other model developer teams with a focus on model inference strategies and productization of those model
  4. Build and optimize GPU-accelerated pipelines for both (customized) model training and inference—prioritizing performance, scalability, and reliability.

Skills

Required

  • MS or PhD in Computer Science, Machine Learning, or a related field—or equivalent industry experience.
  • 10+ years of experience in machine learning, including production-scale deployments.
  • 3+ years of experience leading large-scale, GPU-intensive GenAI systems (training, inference, and optimization).
  • Experience with GenAI frameworks and tools such as PyTorch, CUDA, Triton, TensorRT, Nvidia Dynamo, and Python.
  • Good understanding of generative model architectures, including diffusion models, transformers, and GANs.
  • Good communication and leadership skills, with a track record of driving alignment in matrixed organizations.

Nice to have

  • Experience with model serving, inference, orchestration, and GPU resource management in large-scale environments.
  • Hands-on expertise in Kubernetes, distributed systems, and MLOps platforms.

What the JD emphasized

  • production-scale deployments
  • large-scale, GPU-intensive GenAI systems (training, inference, and optimization)

Other signals

  • Generative AI Services
  • scalable, high-performance generative AI systems
  • design and develop efficient inference pipelines
  • optimize models for latency and through at inference
  • build APIs and ecosystems that integrate generative models
  • production-scale deployments
  • large-scale, GPU-intensive GenAI systems (training, inference, and optimization)