Senior Machine Learning Engineer - Account Takeover
Abnormal Security is looking for a Senior 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 17% of the Fortune 500. We continue to grow and innovate to stay ahead of the evolving threat landscape. About 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 About the Role
This role will have an opportunity to have a significant impact on the overall charter, direction and roadmap of the team. You will be involved in defining the technical roadmap required to address the most pressing customer problems and simultaneously, maintain production models ensuring operational excellence as well as long term strategy. The ideal candidate will have a strong 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
Lead the development of machine learning algorithms and models for behavioural 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. Actively monitor and improve production models through feature engineering, rules and ML modeling. Participate in code reviews and ensure high quality and maintainability of ML systems. Stay updated on the latest research in the field of machine learning, data science, and AI. Contribute to the development of machine learning best practices within the organization. Provide mentorship and guidance to junior team members. Required Skills:
Proven experience as a Machine Learning Engineer or similar role. Strong knowledge of machine learning algorithms, statistics, and predictive modeling Proficiency with Python and machine learning toolkits like pandas, scikit-learn, and optionally pytorch/tensorflow. Experience with machine learning operations (MLOps) and productionization of ML models. 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 complex 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) #LI-LB1
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Abnormal Security is looking for a Senior 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 17% of the Fortune 500. We continue to grow and innovate to stay ahead of the evolving threat landscape. About 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 About the Role
This role will have an opportunity to have a significant impact on the overall charter, direction and roadmap of the team. You will be involved in defining the technical roadmap required to address the most pressing customer problems and simultaneously, maintain production models ensuring operational excellence as well as long term strategy. The ideal candidate will have a strong 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
Lead the development of machine learning algorithms and models for behavioural 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. Actively monitor and improve production models through feature engineering, rules and ML modeling. Participate in code reviews and ensure high quality and maintainability of ML systems. Stay updated on the latest research in the field of machine learning, data science, and AI. Contribute to the development of machine learning best practices within the organization. Provide mentorship and guidance to junior team members. Required Skills:
Proven experience as a Machine Learning Engineer or similar role. Strong knowledge of machine learning algorithms, statistics, and predictive modeling Proficiency with Python and machine learning toolkits like pandas, scikit-learn, and optionally pytorch/tensorflow. Experience with machine learning operations (MLOps) and productionization of ML models. 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 complex 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) #LI-LB1
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