Engineering Manager, Abnormal Security

• 2+ years experience of managing data-driven (ML) product teams running at large scale data ( 100M+ ) and ability to guide a team technically in this respect
• 3+ years experience designing, building and deploying machine learning applications in one of the domains of text understanding, entity recognition, NLP experience, computer vision, recommendation systems, or search
• 4+ years of hands-on experience in building and safely shipping backend-heavy product ML adjacent systems at high velocity
• Ability to understand business requirements thoroughly and bias toward guiding the team to build a simplest yet generalizable ML model/system that can accomplish the goal
• Metrics-driven culture: Has led a team of engineers in building out systems and displayed the ability to define metrics of excellence and setup processes to continuously monitor and maintain high standards on metrics
• Be Customer-obsessed: Worked with multiple stakeholders to gather requirements and then prioritize and balance against the team's roadmap
• Set High standards - sets high standards and expectations for project execution for themselves and the whole team

Desirable

• Abnormal Security is looking for a Machine Learning Engineering Manager to lead the Attack Detection team
• The EM will report to the Senior EM of the Message Detection Team, and will lead a team that is primarily composed of machine learning engineers
• The EM will be responsible for managing the execution of the roadmap and deliverables while optimizing both human and system resource utilization
• The Engineering Manager’s success or failure impacts our ability to build and iterate on the detection decisioning system at an extremely high recall, enabling us to respond to current and future attacks
• Own the execution success of the quarterly roadmap for the Attack Detection team; engage continuously with the Tech Leads to help adjust and prioritize current roadmap items according to the team’s charter and company priorities
• Deliver on:
• Extremely high recall detector engine in an adversarial environment using model-based as well as heuristic detectors
• Automated model retraining pipelines based continuous ML deployment system to automatically ‘learn’ from new patterns
• Text-based signal models to capture and model suspiciousness of email content in multiple languages
• Own both directly customer-impacting metrics and system metrics and able to work with the team to proactively identify new attacks and repair degradations:
• Recall ie False Negative metrics for our customer base across various attack types
• Overall attack and precision metrics for highly flagging generalized ML models
• Automation metrics for responses to customer-specific requirements
• Own the machine learning feature consumption layer for both message metadata/content and current/historical user behavior type of signals and and systems and processes to continuously incorporate new features types into existing and new ML models
• Drive processes to enable the team to deliver on projects that are set by TLs as part of the technical roadmap
• Manage the quarterly roadmap updates, project time estimates, weekly sprint planning, day-to-day standups
• Identify risks on project delivery (technical, operational, dependency risks) and escalate to appropriate technical leads
• Able to assess progress in a metrics-oriented manner
• Provide continuous feedback
• Responsible for mentoring and growing the engineers on the team by providing constructive feedback at regular intervals to help them successfully execute high-impact projects
• Drive Stakeholder alignment: Collaborate with the platform and infrastructure initiatives on company-wide initiatives to help increase efficiency, customer adoption, and engineer effectiveness goals
• Drive stakeholder alignment: Proactively identify and collaborate with platform and infrastructure teams on company-wide initiatives including