Abnormal Security is hiring a
Senior Machine Learning Engineer

Summary

Join Abnormal Security as a Senior Machine Learning Engineer to design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product. The role involves identifying new features groups or ML model approaches to improve detection efficacy, working with infrastructure & systems engineers to productionize signals, understanding features that distinguish safe emails from email attacks, and more.

Requirements

• Track record of success in translating business requirements into scalable, maintainable systems with a bias toward simpler but iterative systems
• 4+ Experience with production ML systems - understands the pillars of a modern ML stack and the development, maintenance and tuning processes of ML models
• Uses a systematic approach to debug both data and system issues within ML / heuristics models
• Fluent with Python and machine learning libraries like numpy and scikit-learn
• Experience with data analytics and wielding SQL+pandas+spark framework to both build data and metric generation pipelines, and answer critical questions about system efficacy or counterfactual treatments
• Independently responsible for the entire lifecycle of projects or features including eng design, development, and deployment
• Works well with other stakeholders - has worked with cross-functional teams to drive projects over the finish-line
• Machine learning academic background (Bachelor's degree in Computer Science or related fields)

Responsibilities

• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product
• Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system
• Understand features that distinguish safe emails from email attacks, and how our detector stack enables us to catch them
• Be the expert in main detection pipelines and decision data flow to be able to drive debugging in systematic degradations caused by bad detectors
• Writes code with testability, readability, edge cases, and errors in mind
• Train models on well-defined datasets to improve model efficacy on specialized attacks
• Actively monitor and improve False Positive rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling
• Analyze False Negative and False Posi datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy
• Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises
• Lead the team’s medium and long term roadmap and drive planning and execution strategy for the pod
• Coach and mentor junior engineers to uplevel their code quality and ML effectiveness by providing quality code reviews and design reviews
• Participate in building a world-class detection engine across all layers - data quality, feature engineering, model development, experimentation and operation

Preferred Qualifications

• MS degree in Computer Science, Electrical Engineering or other related engineering field
• Experience with big data or statistics
• Familiarity with cyber security industry