Senior Machine Learning Engineer, Abnormal Security

• You are someone who wants to make an impact
• You are passionate about solving customer problems and have a burgeoning set of skills around machine learning, software engineering and data science with which to do so
• You want to apply those skills on a problem that leaves the world in a better place
• You are humble and want to learn!
• This is one of your first jobs - maybe your actual first job - and you know that there’s a ton of skills to build and knowledge to grow and you want to do so as fast as possible
• You ask questions. You take notes
• You have an active and curious approach to your work and as a result you grow faster than the average person
• Strong software fundamentals - has ample experience coding in a production environment
• Experience designing systems with data at their center - has thought through and executed on long-term designs to support a scaling product
• Experience with production ML systems - understands the pillars of a modern ML stack and the development/maintenance process of ML models
• Fluent with Python and machine learning libraries like numpy and scikit-learn
• Familiarity with using data processing frameworks like Pandas and Spark
• Systematic approach to debug both data and system issues with ML models or heuristics
• Writing code that is easily testable and understood by other engineers
• Works well with other stakeholders - has worked with cross-functional teams to drive projects over the finish-line
• Machine learning academic background (Bachelor's degree in Computer Science or related fields)
• Hands on experience training and tuning models
• 5+ years of experience applying these skills in a production environment
• Interest in security and stopping bad actors

Desirable

• We’re looking to add a Senior Machine Learning Engineer to our Account TakeOver (ATO) Detection team
• The ATO Detection team’s mission statement is to provide world-class attack detection efficacy to tackle the fast evolving attack landscape using a combination of generalizable and auto-trained models as well as specific detectors for high value attack categories
• This team is solving a multi-layered detection problem - from modeling communication patterns to establish enterprise-wide baselines, to normalizing across multiple event sources, to making use of contextual information to avoid false positives (e.g. comparing unusual sign in locations with travel records)
• This role straddles the line between velocity and excellence
• We are also not only a remote team, but a very distributed team, as such you will need to have excellent communication skills across both verbal and written mediums
• You will need to be just as comfortable on a zoom call as writing a 1 pager project proposal to be shared across the team for technical feedback
• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an ATO detection product, with guidance from senior engineers
• Build attack detection systems capable of highlighting rare, suspicious activity (one in a million) with 95%+ precision & <1 minute latency on the event stream
• Understand the nature of attacks and design features to calibrate behavior across our customers from multiple industries, with different usage patterns to provide consistent performance
• Write code with testability, readability, edge cases, and errors in mind, such as feature drifts between online/offline data
• Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises
• Participate in building a world-class detection engine across all layers - data quality, feature engineering, model development, experimentation and operation
• Work with infrastructure & systems engineers to develop the right feature aggregates to feed into the detection system
• Create a magical work environment with colleagues and memorable interview process for candidates