Senior Machine Learning Engineer

Summary

Join Abnormal Security as a Senior Machine Learning Engineer on the Account Takeover Detection team, where you'll play a crucial role in protecting customers from financial losses caused by malicious attacks. You'll be responsible for developing and implementing cutting-edge machine learning models and systems to proactively detect and prevent account takeovers, ensuring the security of user accounts with unparalleled accuracy and efficiency. This role offers the opportunity to significantly impact the team's direction and roadmap, addressing pressing customer problems while maintaining operational excellence and long-term strategy. You'll collaborate with cross-functional teams, conduct data analysis and model development, and work with infrastructure and product engineers to productionize models and features. You'll also actively monitor and improve production models, participate in code reviews, stay updated on the latest research, and contribute to the development of machine learning best practices within the organization.

Requirements

• 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

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

Preferred Qualifications

• 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)