Mid-Level Machine Learning Developer

1 week ago

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

Abnormal Security is seeking a skilled Machine Learning Engineer to become a vital member of the Message Detection - Attack Detection team. Our mission is to safeguard our clients from sophisticated threats that continuously adapt their strategies to bypass conventional security measures. This is where our innovative behavioral-based methodology stands out.

In an environment where a single successful breach can result in substantial financial repercussions, the Attack Detection team is tasked with developing a highly efficient Detection Engine capable of processing vast volumes of messages with minimal latency. Our goal is to deliver exceptional detector performance to address the evolving threat landscape through a blend of adaptable and auto-trained models, alongside specialized detectors for critical attack types.

This team tackles a complex detection challenge, which includes analyzing communication behaviors to establish comprehensive baselines across enterprises, utilizing these behaviors as reliable indicators, and merging these indicators with contextual data to create precise detection systems. We generate discriminative signals at multiple levels, including message-level (e.g., identifying specific phrases), sender-level (e.g., analyzing sender frequency), and recipient-level (e.g., assessing the likelihood of receiving a secure message). These signals are then integrated to train highly accurate model-based and heuristic detectors. To remain agile against new, unseen threats, the team also develops various stages in our automated model retraining pipelines, encompassing data analytics, modeling, production evaluation, and deployment.

This position offers the chance to significantly influence the team's overall strategy, direction, and roadmap. The Machine Learning Engineer will engage in understanding the domain of false negatives, specifically the current and potential threats that could disrupt customer workflows. They will help outline the technical roadmap necessary to tackle the most urgent customer challenges while maintaining our detection system's high recall rate.

Key Responsibilities
  • Design and implement systems that integrate rules, models, feature engineering, and business insights into an email detection solution, under the guidance of senior engineers.
  • Comprehend the features that differentiate secure emails from malicious ones and how our model stack enables effective detection.
  • Identify and propose new feature groups or machine learning model strategies that can significantly enhance detection performance 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 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 technology stack, such as building and troubleshooting data pipelines, or presenting findings to clients when necessary.
Required Qualifications
  • 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 technologies.
  • 1+ years of experience in developing stable, production-level pipelines for model training and evaluation, leading to reproducible models and metrics.
  • Proficient in data analytics and utilizing SQL, pandas, and Spark frameworks to construct data and metric generation pipelines, as well as addressing critical questions regarding system performance.
  • Ability to thoroughly understand business requirements and a preference for designing simple yet generalizable machine learning models and systems that achieve objectives.
  • Employ a systematic approach to troubleshoot both data and system issues within machine learning and heuristic models.
  • Fluent in Python and familiar 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 discipline.
Preferred Qualifications
  • 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.
  • A research-focused role that is disconnected from product or customer needs.
  • A statistics/data science role that intersects with machine learning.

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

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