About the job
We are looking for a Senior Machine Learning Engineer to power the intelligence inside our products. This is a hands-on modeling role for someone proficient in the full stack: classical discriminative to interpret, generative to produce, and agentic to act. Join us and build Adobe’s future products!
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, from predictive and ranking to generative and agentic systems, including RAG, embeddings, fine-tuning, and in-product copilots for creative use cases.
Own evaluation end to end: define what "good" means, build offline and online evaluation harnesses, and detect quality regressions in production.
Build the data and MLOps foundations that keep models reliable: feature pipelines, experiment tracking, versioning, CI/CD, automated retraining, and monitoring.
Qualifications
Minimum
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 on top of that: 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.
Preferred
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.