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Mid-Level Machine Learning Engineer
2 months ago
Abnormal Security is seeking a skilled Machine Learning Engineer to contribute to the Message Detection - Attack Detection team. Our mission is to safeguard our clients from evolving threats posed by adversaries who continuously adapt their strategies to bypass conventional security measures. Our innovative behavioral-based approach has earned us recognition as a leading cybersecurity startup, and our AI-driven system has garnered numerous accolades, establishing us as a trusted protector of a significant portion of Fortune 500 companies.
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 hundreds of millions of messages with minimal latency. The team's objective is to deliver exceptional detector performance to address the dynamic threat landscape by leveraging a combination of generalizable models and specialized detectors tailored for high-value attack scenarios.
This team tackles a complex detection challenge, which includes modeling communication patterns to establish comprehensive baselines across enterprises, utilizing these patterns as robust indicators, and integrating contextual information to create highly accurate systems. We develop discriminative signals at various 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 amalgamated to train precise model-based and heuristic detectors. Furthermore, to adapt to new and unseen threats, the team constructs various stages in our automated model retraining pipelines, encompassing data analytics, modeling, production evaluation, and deployment stages.
This position offers a unique opportunity to significantly influence the team's overall strategy, direction, and roadmap. The Machine Learning Engineer will engage in understanding the domain of false negatives, focusing on current and future threats that could disrupt customer workflows. They will play a crucial role in defining the technical roadmap necessary to tackle the most urgent customer challenges while ensuring our detection decision-making system operates at an exceptionally high recall rate.
Key Responsibilities- Design and implement systems that integrate rules, models, feature engineering, and business insights into an email detection product, under the guidance of senior engineers.
- Comprehend the features that differentiate secure emails from threats and understand how our model stack enables effective detection.
- Identify and propose new feature groups or machine learning approaches that can 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, readability, and error handling.
- Train models on well-defined datasets to enhance efficacy against specialized threats.
- Continuously monitor and improve false negative rates and efficacy 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, including building and troubleshooting data pipelines and presenting findings to clients when necessary.
- 3+ years of experience in designing, building, and deploying machine learning applications in areas such as text understanding, 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, ensuring reproducible models and metrics.
- Proficient in data analytics and utilizing SQL, pandas, and Spark frameworks to construct data and metric generation pipelines, addressing 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.
- 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 field.
- 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.
Note: This position is not focused on optimizing existing machine learning models, nor is it a research-oriented role detached from product or customer interaction. It is not a statistics/data science position that intersects with machine learning.
