Senior ML Engineer — Security AI & Production Systems

Adobe; San Jose (CA); On-site; USD 212,000 - 307,000; Full time

Job summary

Adobe Security Engineering seeks a Staff Machine Learning Engineer to design, build, and scale production ML systems across large security data using deep learning, behavioral modeling, embeddings, and agentic AI. You will train models, run experiments, and shape architecture for the broader team while partnering with data, platform, and security engineers.

You will own the ML lifecycle from feature engineering through training, deployment, monitoring, and retraining, and contribute to

Qualifications

• MS or PhD in computer science, machine learning, or a related field, or equivalent practical experience.
• Strong background training models with PyTorch and transformers, including behavioral modeling and anomaly detection.
• Experience with distributed compute (e.g., Spark) and cloud platforms (AWS).
• Proficient in Python and SQL, with solid testing and code-review habits.
• Experience with LLMs or generative AI, and applying ML to security-related problems.

Responsibilities

• Design, build, and scale production ML systems spanning DL, behavioral modeling, embeddings, and agentic AI.
• Own the ML lifecycle: feature engineering, training, deployment, monitoring, retraining.
• Collaborate with data, platform, and security engineers; mentor engineers and guide technical direction.

Skills

PyTorch

Transformers

Behavioral modeling

Anomaly detection

Spark

AWS

Python

SQL

Testing

Code review

Mentoring

Education

MS or PhD in CS/ML or related field

Tools

MLflow

Vector databases

RAG

Multi-agent systems

Job description

Adobe Security Engineering seeks a Staff Machine Learning Engineer to design, build, and scale production ML systems across large security data using deep learning, behavioral modeling, embeddings, and agentic AI. You will train models, run experiments, and shape architecture for the broader team while partnering with data, platform, and security engineers.

You will own the ML lifecycle from feature engineering through training, deployment, monitoring, and retraining, and contribute to