Machine Learning Engineer 4
Adobe · Bengaluru · Posted 2026-07-14
Tech stack: AWS, Azure, GCP, Kubernetes, Python
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About the role
Adobe Content Intelligence helps marketers and creators understand what makes their work perform. Our team builds the models and pipelines that analyze images, video, email, and paid social ads — then connect those signals to campaign results. That work powers insights, recommendations, and agent tools used across Adobe's products today. We're looking for a senior Machine Learning Engineer to own how those systems are designed, built, and shipped. You'll train and improve models yourself — not just plug in models someone else built — and you'll shape the architecture for multi-modal creative intelligence at enterprise scale. If you like turning hard ML problems into production systems that real customers use, we'd love to meet you! What you'll do 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.
Qualifications
- 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.
Responsibilities
- 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.