Machine Learning Engineer

Adobe•San Francisco, CA

1d•Remote

About The Position

The Pro Design team at Adobe is transforming the world of design with AI-powered tools for professional creators and teams. We are seeking a Senior Machine Learning Engineer to develop the intelligence within our products, focusing on classical discriminative, generative, and agentic systems. This role involves hands-on modeling and contributing to the future of Adobe's creative software.

Requirements

Bachelor's in a quantitative field (CS, ML, Data Science, Engineering, or similar) or equivalent practical experience.
5+ years building, deploying, and operating ML systems in production at scale.
Strong core ML foundation: feature engineering and shipping supervised/unsupervised models for prediction, ranking, recommendation, or personalization.
Working depth in modern GenAI: LLMs or generative models in production (RAG, embeddings, fine-tuning, or agent design).
Fluency in Python and SQL, and broader ML tooling and infrastructure ecosystem.
Rigorous offline and online evaluation and experimentation design and operation.
Ability to frame problems before modeling, and to communicate technical work clearly to non-technical Product and Design partners.

Nice To Haves

Master's or PhD in a quantitative discipline.
Background in consumer product analytics, especially in creative tools, SaaS, or subscription businesses.
Experience with recommendation systems or personalization, especially for visual or creative content.
Experience deploying and monitoring models in Databricks (MLflow, Feature Store, model serving) or a comparable production ML environment.

Responsibilities

Partner with Product, Engineering, and Data Science teams to define problems, scope feasibility, and translate research into shipped experiences.
Design, prototype, and ship models powering product features, including predictive, ranking, generative, and agentic systems (RAG, embeddings, fine-tuning, in-product copilots).
Own end-to-end model evaluation: define success metrics, build offline and online evaluation harnesses, and detect quality regressions in production.
Build and maintain data and MLOps foundations for model reliability: feature pipelines, experiment tracking, versioning, CI/CD, automated retraining, and monitoring.