Machine Learning Engineer 4

Adobe; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

Adobe is looking for a Machine Learning Engineer to join its Genuine Engineering team in Bengaluru, India. In this role, you will build and optimize machine learning models to combat fraud and protect user experiences. Key duties include managing the entire model lifecycle, collaborating across teams, and mentoring fellow engineers.

The ideal candidate has over 8 years of experience in machine learning and a strong grasp of model optimization. Adobe offers a collaborative work environment and opportunities for innovation.

Qualifications

• 8+ years of professional experience building and deploying ML solutions at scale.
• Strong programming expertise in Python, with hands-on experience in machine learning frameworks.
• Deep understanding of the end-to-end ML lifecycle.

Responsibilities

• Build and train deep learning models including custom transformer architectures.
• Translate prototypes into scalable production ML systems.
• Collaborate cross-functionally with data science and product teams.

Skills

Python

PyTorch

TensorFlow

Machine Learning

Data Science

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Databricks

Spark

Job description

About the Role

Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. 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. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large‑scale GPU training, deployment, and monitoring.

Key Responsibilities

• Build and train deep learning models from scratch, including custom transformer and attention‑based architectures for long behavioral event sequences.
• Own the full training stack: event tokenization, temporal and positional embeddings, self‑supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine‑tuning.
• Train large models efficiently on GPU infrastructure using mixed‑precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent).
• Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high‑quality model inputs.
• 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.
• Stay current with advances in ML/AI and bring relevant innovations into Adobe's products.

Minimum Qualifications

• Bachelor’s degree or Master’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field.
• 8+ years of professional experience building and deploying ML solutions at scale.
• 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.

Preferred Qualifications

• Experience in fraud detection, anomaly detection, or behavioral modeling.
• Exposure to Adobe Experience Platform (AEP) or other large‑scale SaaS ecosystems.

EEO Statement

Adobe is an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic.