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AI Systems Engineer II

2 months ago

New York, New York, United States Abnormal Security Full time
Job Overview

Abnormal Security is seeking a talented Machine Learning Engineer to become a part of our Message Detection - Attack Detection team. Our mission is to safeguard our clients from sophisticated threats that continually adapt their strategies to bypass conventional security measures. Our innovative behavioral-based methodology sets us apart in the cybersecurity landscape.

In an environment where a single successful breach can result in substantial financial repercussions, the Attack Detection team is pivotal in developing a high-recall Detection Engine capable of processing vast volumes of messages with minimal latency. The team is dedicated to delivering exceptional detector performance to address the evolving threat landscape through a mix of generalizable and auto-trained models, alongside specialized detectors for critical attack categories.

This team tackles a complex detection challenge, which includes modeling communication behaviors to establish comprehensive baselines across enterprises, integrating these behaviors as robust indicators, and merging these indicators with contextual data to develop highly accurate systems. We create discriminative signals at multiple levels, including message-level (e.g., specific phrases), sender-level (e.g., sender frequency), and recipient-level (e.g., probability of receiving a secure message). These signals are then utilized to train both model-based and heuristic detectors. To remain agile against new, unseen threats, the team is also responsible for developing various stages of our automated model retraining pipelines, which encompass data analytics, model training, production evaluation, and automated deployment.

This position offers the chance to significantly influence the team's overall objectives, direction, and strategic planning. The Machine Learning Engineer will engage in identifying the domain of false negatives, focusing on current and future threats that could disrupt customer workflows. They will contribute to defining the technical roadmap necessary to tackle the most urgent customer challenges while ensuring our detection decision-making system operates with high recall.

Key Responsibilities
  • Design and implement systems that integrate rules, models, feature engineering, and business insights into an email detection solution, with guidance from senior engineers.
  • Comprehend the features that differentiate secure emails from threats and understand how our model architecture captures these distinctions.
  • Identify and propose new feature groups or machine learning approaches that could significantly enhance detection effectiveness for our products. Collaborate with infrastructure and systems engineers to operationalize signals for the detection system.
  • Write code with a focus on testability, clarity, edge cases, and error handling.
  • Train models on well-defined datasets to enhance model performance against specialized threats.
  • Continuously monitor and improve false negative rates and effectiveness rates for our message detection product's attack categories through feature engineering, rules, and machine learning modeling.
  • Analyze false negative and false positive datasets to identify capability gaps and suggest immediate feature and rule enhancements to boost detection performance.
  • Contribute to other areas of the stack, such as building and troubleshooting data pipelines or presenting findings to clients when necessary.
QualificationsMust Have
  • 3+ years of experience in designing, building, and deploying machine learning applications in areas such as text analysis, entity recognition, natural language processing, computer vision, recommendation systems, or search.
  • 1+ years of experience in developing stable, production-level pipelines for model training and evaluation, leading to reproducible models and metrics.
  • Proficiency in data analytics and experience with SQL, pandas, and the Spark framework to construct data and metric generation pipelines, as well as to address critical questions regarding system performance.
  • Ability to thoroughly understand business requirements and a tendency to design the simplest yet most generalizable machine learning models/systems to achieve objectives.
  • A systematic approach to debugging both data and system issues within machine learning and heuristic models.
  • Fluency in Python and familiarity with machine learning toolkits such as NumPy, scikit-learn, PyTorch, and TensorFlow.
  • Strong software engineering skills, capable of quickly finding solutions within the codebase and producing structured, readable, well-tested, and efficient code.
  • Bachelor's degree in Computer Science, Applied Sciences, Information Systems, or a related engineering field.
Nice To Have
  • Master's degree in Computer Science, Electrical Engineering, or a related engineering field.
  • Experience with big data, statistics, and machine learning.
  • Familiarity with algorithms and optimization techniques.

This position does not involve:

  • Optimizing existing machine learning models.
  • Research-oriented tasks that are distanced from product or customer interaction.
  • A role that merges statistics/data science with machine learning.

At Abnormal Security, certain roles may be eligible for bonuses, restricted stock units (RSUs), and various benefits. Compensation packages are tailored to each candidate, taking into account their skills, experience, qualifications, and other job-related factors. We recognize that benefits are a vital component of your overall compensation package.