Machine Learning Engineer, Adobe Firefly Services

Adobe Adobe · Enterprise · San Jose, CA +1

Machine Learning Engineer focused on building and optimizing scalable generative AI services and inference pipelines for Adobe products. The role involves integrating various generative models, optimizing them for performance, and building APIs for product integration.

What you'd actually do

  1. Design and evelopment 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.
  • 4-7+ years of experience in machine learning, including production-scale deployments.
  • 2+ 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)
  • model serving, inference, orchestration, and GPU resource management in large-scale environments

Other signals

  • Generative AI Services
  • scalable, high-performance generative AI systems
  • design and develop efficient inference pipelines
  • optimize models for latency and throughput at inference
  • build APIs and ecosystems that integrate generative models