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Lead Machine Learning Architect
2 months ago
Abnormal Security is seeking a Senior Machine Learning Engineer to be a pivotal member of the Message Detection Decisioning team. Our mission is to safeguard our clients from evolving threats posed by adversaries who continuously adapt their strategies to circumvent traditional security measures. This is where our innovative behavioral-based approach stands out, enabling us to be recognized as a leading entity in the cybersecurity landscape.
In an environment where a single successful breach can result in substantial financial repercussions, the Message Detection Decisioning team is crucial in developing a highly accurate Detection Engine capable of processing hundreds of millions of messages with minimal latency. Every message processed by Abnormal undergoes a thorough evaluation through our workflow, which utilizes numerous signals and detectors tailored to the context of the message and the user. This process culminates in a decisive output that informs various performance metrics.
This team is tackling a complex detection challenge that involves analyzing communication trends to establish comprehensive baselines across enterprises. By integrating these patterns as reliable signals and augmenting them with contextual data, we aim to create systems with exceptional precision. The team engineers signals at multiple levels, including message-specific (e.g., identifying certain phrases), sender-specific (e.g., analyzing sender frequency), and recipient-specific (e.g., assessing the likelihood of receiving a secure message). This foundational work is essential for developing accurate heuristic and model-based detectors. Furthermore, to ensure sustained high precision, the team innovates on software systems and processes that can swiftly adapt to short-term trends while maintaining long-term effectiveness.
This position offers a unique opportunity to significantly influence the team's strategic direction and growth. The Senior Machine Learning Engineer will engage with pressing customer challenges related to false positives and will be responsible for crafting a technical roadmap that ensures our detection decisioning system operates with remarkable precision.
Key Responsibilities- Design and implement systems that integrate rules, models, feature engineering, and business insights into an email detection solution.
- Identify and propose new feature groups or machine learning model strategies that can enhance detection performance for our products.
- Gain a deep understanding of the characteristics that differentiate safe emails from malicious ones, leveraging our detection stack to identify threats.
- Serve as the authority on primary detection pipelines and decision data flow, facilitating systematic debugging of issues arising from suboptimal detectors.
- Write clean, testable, and maintainable code, considering edge cases and potential errors.
- Train models on well-defined datasets to enhance model performance against specialized threats.
- Continuously monitor and refine False Positive rates and efficacy metrics for our message detection product categories through feature engineering and machine learning modeling.
- Analyze datasets related to False Negatives and False Positives to identify capability gaps and recommend actionable feature and rule enhancements.
- Contribute to various aspects of the technology stack, including building and troubleshooting data pipelines, and presenting findings to stakeholders when necessary.
- Lead the medium and long-term strategic planning and execution for the team.
- Mentor junior engineers to elevate their coding standards and machine learning effectiveness through constructive code and design reviews.
- Participate in the development of a world-class detection engine across all layers, focusing on data quality, feature engineering, model development, experimentation, and operational excellence.
- Proven track record in translating business needs into scalable, maintainable systems, favoring simplicity and iterative improvements.
- 4+ years of experience with production machine learning systems, with a solid understanding of modern ML stack components and the lifecycle of model development, maintenance, and tuning.
- Employ a systematic approach to troubleshoot both data and system challenges within machine learning and heuristic models.
- Proficient in Python and familiar with machine learning libraries such as NumPy and Scikit-learn.
- Experience in data analytics, utilizing SQL, Pandas, and Spark frameworks to construct data and metric generation pipelines, and to address critical system efficacy questions.
- Independently manage the complete lifecycle of projects or features, including engineering design, development, and deployment.
- Collaborate effectively with cross-functional teams to drive projects to completion.
- Possess a machine learning academic background, ideally holding a Bachelor's degree in Computer Science or a related field.
- Master's degree in Computer Science, Electrical Engineering, or a related engineering discipline.
- Experience with big data technologies or statistical analysis.
- Familiarity with the cybersecurity industry.
- This position does not focus on optimizing existing machine learning models.
- This is not a research-oriented role that is distanced from product or customer interaction.
- This role is not a blend of statistics/data science and machine learning.
At Abnormal Security, certain roles may be eligible for bonuses, restricted stock units (RSUs), and comprehensive 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.
