Staff Machine Learning Engineer

Adobe

Staff Machine Learning Engineer building production ML, LLM, and agentic AI systems for Adobe cybersecurity. Designing threat detection, investigation, and security analytics capabilities at scale.

Posted 9/11/2026full-timeSan Jose • California • 🇺🇸 United StatesLead💰 $211,800 - $306,625 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and scaling production ML systems, particularly in deep learning and agentic AI for security applications. Proficient in the ML lifecycle, from feature engineering to deployment, with a strong focus on collaboration and mentorship within engineering teams.

Highest-signal resume keywords

Production ML Systems ManagementPyTorch and Transformers ProficiencyDistributed Computing with SparkCloud Platforms Experience (AWS)MLOps Tools (MLflow)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningBehavioral ModelingFeature EngineeringModel TrainingAnomaly DetectionPython ProgrammingSQL ProficiencySoftware Engineering PracticesVector DatabasesGenerative AI

Soft Skills

MentoringCollaborationTechnical Direction

Tools & Technologies

MLflowAWSSpark

Certifications & Qualifications

MS or PhD in Computer ScienceMachine Learning

Industry Keywords

CybersecurityFraud DetectionAdversarial ProblemsAgentic AIRetrieval-Augmented Generation

Tech Stack

Tools & technologies

AWSCloudCyber SecurityPythonPyTorchSparkSQL

About the role

Key responsibilities & impact

Design, build, and scale production ML systems spanning deep learning, behavioral modeling, embeddings, and agentic AI
Architect end-to-end ML systems for high-volume security data
Turn 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 to measure model quality and help analysts trust and validate results
Collaborate with data and platform engineers to scale pipelines and resolve system bottlenecks
Mentor engineers and guide technical direction through design reviews and hands-on collaboration

Requirements

What you’ll need

Experience running production ML systems from early experimentation through sustained operation at scale
Strong background training models with PyTorch and transformers, including behavioral modeling and anomaly detection
Experience with distributed compute such as Spark, cloud platforms such as AWS, and MLOps tools such as MLflow
Experience building with LLMs or generative AI and evaluating their behavior in production
Strong Python and SQL skills
Software engineering practices including testing and code review
MS or PhD in computer science, machine learning, or a related field, or equivalent practical experience
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

Benefits

Comp & perks

Annual Incentive Plan (AIP) for short-term incentives
Certain roles may be eligible for a new hire equity award
Comprehensive benefits programs
Reasonable accommodation during the recruiting process
Equal employment opportunity and accessibility support