Machine Learning Engineer 3

Role overview

Machine Learning Engineer 3 at Adobe

Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs.

In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users.

Responsibilities

• You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring.
• Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization.
• Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and production monitoring.
• Collaborate cross-functionally with data science, product, and platform teams; mentor junior engineers on experimentation rigor, deployment process, and responsible AI.

Required qualifications

• Strong programming expertise in Python, with hands-on experience in PyTorch, TensorFlow, or similar frameworks.
• Deep understanding of the end-to-end ML lifecycle—from data collection to deployment and monitoring.
• Strong grasp of model optimization, inference efficiency, and production system integration.
• Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Employment details

• Location: Noida [Uttar Pradesh] [India], Bangalore [Karnātaka] [India]

About the employer

• Stay current with advances in ML/AI and bring relevant innovations into Adobe's products.

Additional role details

• Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences.
• Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent).
• Bachelor’s degree or Master’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field. 5+ years of professional experience building and deploying ML solutions at scale.
• Experience in fraud detection, anomaly detection, or behavioral modeling.