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.
The Challenge- 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.
What you bring- 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 have- 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.
Expected Pay Range:Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $172,500 -- $306,625 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.
In California, the pay range for this position is $211,800 - $306,625
At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).
In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
