Machine Learning Engineer II

1 week ago

New York, New York, United States Abnormal Security Full time
About the Role

Abnormal Security is seeking a skilled Machine Learning Engineer to join the Message Detection - Attack Detection team. Our innovative behavioral-based approach to security has earned us recognition as one of the top cybersecurity startups, and we're looking for talented individuals to help us continue to innovate and protect our customers.

The Attack Detection team plays a critical role in building a high-recall detection engine that can operate on hundreds of millions of messages at milliseconds latency. As a Machine Learning Engineer, you will be responsible for designing and implementing systems that combine rules, models, feature engineering, and business and product inputs into an email detection product.

Key responsibilities include understanding features that distinguish safe emails from email attacks, identifying and recommending new features or ML model approaches to improve detection efficacy, and working with infrastructure & systems engineers to productionize signals to feed into the detection system.

This is a unique opportunity to have a significant impact on the overall charter, direction, and roadmap of the team. You will be involved in understanding the domain of false negatives, helping to define the technical roadmap required to address pressing customer problems, and operating our detection decisioning system at an extremely high recall.

What You Will Do
  • Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product.
  • Understand features that distinguish safe emails from email attacks.
  • Identify and recommend new features or ML model approaches to improve detection efficacy.
  • Work with infrastructure & systems engineers to productionize signals to feed into the detection system.
  • Train models on well-defined datasets to improve model efficacy on specialized attacks.
  • Analyze FN and FP datasets to categorize capability gaps and recommend short-term feature and rule ideas to improve detection efficacy.
Requirements
  • 3+ years of 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.
Nice to Have
  • 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.

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