remote

Machine Learning Engineer II - Behavioral Security Products

Abnormal AI is seeking a Machine Learning Engineer II to join the Account Takeover Detection team. This role involves developing and productionizing machine learning models for behavioral security products, conducting data analysis, and collaborating with cross-functional teams to combat evolving cyber threats. The ideal candidate will have 3+ years of experience in ML engineering and proficiency in Python and ML toolkits.

About the role

About Abnormal AI

Abnormal AI is a leader in cybersecurity, protecting customers against evolving threats with advanced AI. Recognized as a Leader in the 2024 Gartner Magic Quadrant for Email Security Platforms, Abnormal secured $250 million in Series D funding at a $5.1 billion valuation in August 2024. The company boasts 100% YoY growth in annual recurring revenue and protects over 20% of the Fortune 500.

The Team

The Account Takeover (ATO) Detection team is crucial in protecting customers from significant financial losses due to account takeovers. Their mission is to leverage cutting-edge machine learning technologies for proactive detection and prevention of ATO attempts, continuously improving capabilities to stay ahead of evolving fraud patterns and safeguard user accounts with unparalleled accuracy and efficiency.

The Role

This role provides a significant opportunity to contribute to the team's charter, direction, and roadmap by defining technical goals, addressing customer problems, maintaining production models, and ensuring operational excellence. The ideal candidate will possess a strong background in machine learning, data science, and software engineering, with the capability to design, develop, and implement robust machine learning models and systems in a production environment.

Key Responsibilities

Contribute to the development of machine learning algorithms and models for behavioral modeling and cybersecurity attack detection.
Collaborate with cross-functional teams to understand requirements and translate them into effective machine learning solutions.
Conduct exploratory data analysis, feature engineering, model development, and evaluation.
Work with infrastructure & product engineers to productionize models and new ML-based features.
Monitor and improve production models through feature engineering, rules, and ML modeling as part of a team effort.
Participate in code reviews to ensure the quality and maintainability of ML systems.
Stay updated on the latest research in the field of machine learning, data science, and AI.
Adopt and contribute to the development of machine learning best practices within the organization.

Required Skills

Proven experience as a Machine Learning Engineer or similar role in a commercial environment (3+ years).
Knowledge of machine learning algorithms, statistics, and predictive modeling.
Proficiency with Python and machine learning toolkits like pandas, scikit-learn, and optionally pytorch/tensorflow.
Awareness of machine learning operations (MLOps) and productionization of ML models best practices.
Familiarity with building data and metric generation pipelines, using tools like SQL or Spark, to answer business questions and assess system efficacy.
Ability to communicate technical ideas in a clear, non-technical manner.

Optional Skills

Familiarity with LLMs.
Previous experience in Cybersecurity.
Previous experience with Airflow or similar ML pipeline orchestration tools.
Experience with large scale ML system and data infrastructure.
Previous experience in behavioural modeling techniques.
PhD or equivalent proven experience in ML research.
Familiarity with cloud computing platforms (AWS, Azure).