机器学习工程师 II - 行为安全产品
Machine Learning Engineer II - Behavioral Security Products
Abnormal AI 正在寻找一名机器学习工程师加入账户接管检测团队。在 Abnormal，我们保护客户免受恶意对手的威胁，这些对手不断演变他们的技术与策略，以绕过传统的安全方法。Abnormal 被评为顶级网络安全初创公司（2024 年 Gartner 魔法象限电子邮件安全平台领导者），于 2024 年 8 月以 51 亿美元估值完成 D 轮融资，融资金额达 2.5 亿美元。我们年度经常性收入实现 100% 的同比增长，这体现了我们的行为 AI 系统在保护超过 20% 的财富 500 强企业方面获得的信任。我们持续增长和创新，以保持对不断变化的威胁环境的领先优势。
职位介绍
在单次成功攻击可能导致数百万美元损失的环境中，账户接管团队（ATO）是客户保护的前沿，发挥着构建能够检测恶意活动并防止账户接管系统的核心作用。账户接管检测团队的使命是利用最前沿的机器学习技术，主动检测和预防账户接管尝试，持续提升 ATO 能力，以应对不断变化的欺诈模式，并以无与伦比的准确性和效率保护用户账户。
此职位将有机会通过定义技术目标、解决客户问题、维护生产模型并确保运营卓越，对团队的章程、方向和路线图做出重大贡献。理想的候选人应具备机器学习、数据科学和软件工程背景，能够设计、开发和在生产环境中实施稳健的机器学习模型和系统。
工作职责
- 参与行为建模和网络安全攻击检测的机器学习算法和模型的开发。
- 与跨职能团队合作，理解需求并将其转化为有效的机器学习解决方案。
- 进行探索性数据分析、特征工程、模型开发和评估。
- 与基础设施和产品工程师合作，将模型和新的基于 ML 的功能投入生产。
- 作为团队协作的一部分，通过特征工程、规则和 ML 建模来监控和改进生产模型。
- 参与代码审查，以确保 ML 系统的质量和可维护性。
查看英文原文
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.
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.
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 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.
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 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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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.