Abnormal AI is looking for a Machine Learning Engineer to join the Account Takeover Detection team. At Abnormal, we protect our customers against nefarious adversaries who are constantly evolving their techniques and tactics to outwit and undermine the traditional approaches to Security. Abnormal is recognized as a top cybersecurity startup (Leader in the 2024 Gartner Magic Quadrant for Email Security Platforms), securing a Series D funding of $250 million at a $5.1 billion valuation in August 2024. Our 100% YoY growth in annual recurring revenue highlights the trust our behavioral AI system has earned in protecting over 20+% of the Fortune 500. We continue to grow and innovate to stay ahead of the evolving threat landscape.
**The Team **
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
The Role
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.
Key Responsibilities
- 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.
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 practise..
- 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
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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.