Abnormal
Machine Learning Engineer II - Behavioral Security Products
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About the Role
In a landscape where a single successful attack can lead to financial losses of millions of dollars, the Account Takeover team (ATO) is at the forefront of customer protection, playing a central role in building systems that can detect malicious activity and protect customers from account takeovers. The Account Takeover Detection team’s mission is to leverage cutting-edge machine learning technologies for proactive detection and prevention of account takeover attempts, continuously improving ATO capabilities to stay ahead of evolving fraud patterns and safeguard user accounts with unparalleled accuracy and efficiency.
This role offers the opportunity to contribute significantly to our team's charter, direction, and roadmap by defining technical goals, addressing customer problems, maintaining production models, and ensuring operational excellence. The ideal candidate will have a background in machine learning, data science, and software engineering, with the ability to design, develop, and implement robust machine learning models and systems in production.
What you will do
- Contribute to the development of machine learning algorithms and models for behavioral modeling and cybersecurity attack detection.
- Work 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.
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Must Haves
- 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 practice.
- 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.
Nice to Have
- Familiarity with LLMs.
- Previous experience in Cybersecurity.
- Previous experience with Airflow or similar ML pipeline orchestration tools.
- Experience with large-scale ML systems and data infrastructure.
- Previous experience in behavioral modeling techniques.
- PhD or equivalent proven experience in ML research.
- Familiarity with cloud computing platforms (AWS, Azure).
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A note on AI in our process:
Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore. They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.
Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or other characteristics protected by law. For our EEO policy statement, please click here. If you would like more information on your EEO rights under the law, please click here.
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