Staff Machine Learning Engineer
About The Position
Join Adobe Security Engineering and help build the ML and generative AI capabilities that protect Adobe, our products, and our customers. Our team sits where cybersecurity, large-scale data, and AI meet, building models for anomaly detection, threat detection, investigation, and security analytics across some of Adobe's largest datasets. As a Staff Machine Learning Engineer, you'll design, build, and scale production ML systems spanning deep learning, behavioral modeling, embeddings, and agentic AI. You'll write code, train models, run experiments, and help shape the architecture the broader team builds on. You'll also partner closely with data, platform, and security engineers across Adobe.
Requirements
- Experience running production ML systems from early experimentation through sustained operation at scale.
- A strong background training models with PyTorch and transformers, including behavioral modeling and anomaly detection.
- Comfort with distributed compute like Spark, cloud platforms like AWS, and MLOps tools like MLflow.
- Experience building with LLMs or generative AI, along with evaluating how they behave in production.
- Strong Python and SQL skills, and solid software engineering habits like testing and code review.
- A collaborative approach to mentoring engineers and shaping technical direction across teams.
- MS or PhD in computer science, machine learning, or a related field, or equivalent practical experience.
Nice To Haves
- Background applying ML to cybersecurity, fraud, or similar adversarial problems.
- Experience with vector databases, RAG, or multi-agent systems.
- Publications, patents, or open-source contributions to ML or AI.
Responsibilities
- Architect end-to-end ML systems for high-volume security data, turning experiments into reusable capabilities.
- Own the ML lifecycle, from feature engineering through training, deployment, monitoring, and retraining.
- Build LLM and agentic AI capabilities for security investigation, including retrieval-augmented generation.
- Design evaluation frameworks that measure model quality and help analysts trust and validate results.
- Work with data and platform engineers to scale pipelines and resolve system bottlenecks together.
- Mentor engineers and help guide technical direction through design reviews and hands-on collaboration.
Benefits
- comprehensive benefits programs
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What This Job Offers
Job Type
Full-time
Career Level
Senior
