Software Engineer, ML Tech Transfer

Adobe Adobe · Enterprise · CA · Remote

Software Engineer role focused on the technical transfer of AI/ML models from research to production within Adobe's Camera Raw team. The role involves evaluating models, optimizing them for on-device inference, and integrating them into shipping imaging products like Photoshop and Lightroom. Requires strong C++ and ML integration experience.

What you'd actually do

  1. Own end-to-end integration of ML models into Camera Raw and related Adobe imaging products, from prototype to production.
  2. Partner with ML researchers to evaluate new models for product readiness, including accuracy, latency, memory, and platform constraints.
  3. Design and maintain scalable on-device AI inference pipelines, including model packaging, versioning, artifact management, and runtime integration.
  4. Define integration practices and patterns that improve consistency, quality, and engineering velocity across the team.
  5. Lead code reviews and provide technical direction on ML integration, performance, and long-term maintainability.

Skills

Required

  • Master's degree or Ph.D. in Computer Science, Engineering, AI/ML, or a related field
  • 5+ years of software engineering experience
  • integrating ML models into production systems
  • ML model formats
  • inference runtimes
  • on-device optimization (CoreML, ONNX, TensorRT, or equivalent)
  • Strong C++ skills
  • experience working in performance-sensitive production codebases
  • model packaging
  • artifact management
  • ML versioning workflows
  • work effectively across ML and engineering teams

Nice to have

  • photography
  • image quality
  • image processing
  • computational photography
  • creative imaging pipelines
  • segmentation, generative, or diffusion models in production settings

What the JD emphasized

  • integrating ML models into production systems
  • on-device optimization
  • inference runtimes
  • performance-sensitive production codebases
  • image processing
  • computational photography
  • creative imaging pipelines
  • segmentation, generative, or diffusion models

Other signals

  • integrating ML models into production systems
  • on-device optimization
  • inference pipelines
  • model packaging
  • ML Tech Transfer