Software Engineer, ML Tech Transfer

Adobe•San Jose, CA

11h

About The Position

The Camera Raw team at Adobe is seeking an experienced Software Engineer to own the technical transfer of AI/ML models from research and training into shipping imaging products. This role sits at the intersection of machine learning and production engineering. You will work directly with ML scientists to evaluate and prepare models for product readiness, and with software engineers to integrate, optimize, and ship those models across Adobe's imaging pipeline. Your work will directly shape how AI-powered creative features reach millions of photographers worldwide across Photoshop, Lightroom, and Adobe Camera Raw. As a Software Engineer on the Camera Raw team, you will develop and maintain the systems and practices that bring cutting-edge AI models to life in production, contributing to some of the most widely used creative tools in the world.

Requirements

Master's degree or Ph.D. in Computer Science, Engineering, AI/ML, or a related field required.
5+ years of software engineering experience, with a focus on integrating ML models into production systems.
Deep experience with ML model formats, inference runtimes, and on-device optimization (CoreML, ONNX, TensorRT, or equivalent).
Strong C++ skills and experience working in performance-sensitive production codebases.
Familiarity with model packaging, artifact management, and ML versioning workflows.
Proven ability to work effectively across ML and engineering teams, translating between research and production requirements.
Passionate about photography, image quality, and the craft of building tools that serve photographers and creative professionals.

Nice To Haves

Experience with image processing, computational photography, or creative imaging pipelines is a strong plus.
Familiarity with segmentation, generative, or diffusion models in production settings is a strong plus.

Responsibilities

Own end-to-end integration of ML models into Camera Raw and related Adobe imaging products, from prototype to production.
Partner with ML researchers to evaluate new models for product readiness, including accuracy, latency, memory, and platform constraints.
Design and maintain scalable on-device AI inference pipelines, including model packaging, versioning, artifact management, and runtime integration.
Define integration practices and patterns that improve consistency, quality, and engineering velocity across the team.
Lead code reviews and provide technical direction on ML integration, performance, and long-term maintainability.
Resolve complex integration issues that span ML systems and production C++ codebases.
Mentor engineers and share expertise through design reviews and knowledge transfer sessions.
Collaborate with product, engineering, XD and research teams to align on model needs and integration priorities.