Machine Learning Engineer 4

• Design and train models for creative understanding across vision, video, and language

• Build labels and features from LLMs, subject-matter experts, and user activity data

• Deliver pipelines for ingestion, featurization, training, versioning, and inference at scale

• Validate models with offline benchmarks and online A/B tests before release

• Operate production ML on Spark, Kubernetes, and GPU/CPU environments

• You'll work with product managers, research scientists, and engineers across Adobe to connect ML work to customer value — from performance insights to brand-aware recommendations and context for agent workflows.
• You'll mentor other engineers, lead design reviews, and share what you learn through docs, talks, and technical interviews.

What you need to succeed

• Strong Python and PyTorch for model development and deployment

• Deep learning experience across vision, video, NLP, or generative AI (LLM/VLM fine-tuning, embeddings, or RAG)

• End-to-end ML systems: feature engineering, training pipelines, inference, and production monitoring

• Distributed data processing and cloud platforms (Spark, Kubernetes, GCP, AWS, or Azure)

• Clear communication with senior leaders on ML trade-offs and technical direction

  • You're comfortable owning ambiguous, multi-team problems with little day-to-day direction. Experience with recommendation systems or marketing and creative content platforms is a plus.