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Tier-1 employer, metro location, and desirable ML specialization produce medium competition density.
Deep multi-modal ML and production deployment requirements limit cross-industry transferability, so sensitivity is high.
Mandatory senior ML production skills, PyTorch, LLM/VLM experience, distributed systems, and platform expertise make shortlisting highly strict.
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Design and train multi-modal machine learning models for creative content understanding including vision, video, and language.
Build and operate end-to-end ML pipelines including feature engineering, training, inference, and monitoring at enterprise scale using Spark, Kubernetes, and GPU/CPU environments.
Collaborate with cross-functional teams to translate ML work into customer value insights and lead technical mentorship and design reviews.
Strong proficiency in Python and PyTorch for model development and deployment.
Experience with deep learning in vision, video, NLP, or generative AI (LLM/VLM fine-tuning, embeddings, or retrieval augmented generation).
Hands-on experience with distributed data processing and cloud platforms like Spark, Kubernetes, GCP, AWS, or Azure.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in designing and operating end-to-end ML systems from data ingestion to production inference and monitoring at scale.
Skilled in addressing ambiguous, multi-team problems with minimal supervision, indicating a high degree of ownership and technical leadership.
Familiarity with recommendation systems or marketing and creative content platforms is a plus, indicating domain alignment with Adobe's product focus.