Machine Learning Engineer II

Abnormal AI is a leading cybersecurity startup focused on protecting customers from evolving threats. They are seeking a Machine Learning Engineer to enhance their Attack Detection team, which is responsible for developing a high recall detection engine to combat sophisticated email attacks.

Responsibilities:

• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance
• Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them
• 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
• 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 FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling
• Analyze FN and FP 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

Requirements:

• 3+ years experience designing, building and deploying machine learning applications in one of the domains of text understanding, entity recognition, NLP experience, computer vision, recommendation systems, or search
• 1+ years of experience with writing stable and production level pipelines for model training and evaluation leading to reproducible models and metrics
• 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
• Ability to understand business requirements thoroughly and bias toward designing a simplest yet generalizable ML model / system that can accomplish the goal
• Uses a systematic approach to debug both data and system issues within ML / heuristics models
• Fluent with Python and machine learning toolkits like numpy, sklearn, pytorch and tensorflow
• Effective software engineering skills who can find answers quickly from code base and writes structured, readable, well tested and efficient code
• BS degree in Computer Science, Applied Sciences, Information Systems or other related engineering field
• MS degree in Computer Science, Electrical Engineering or other related engineering field
• Experience with big data, statistics and Machine Learning
• Experience with algorithms and optimization