Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance.
Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them.
Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system.
Writes code with testability, readability, edge cases, and errors in mind.
Train models on well-defined datasets to improve model efficacy on specialized attacks
Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling.
Analyze FN and FP datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy.
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

Requirements

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.
1+ years of experience with writing stable and production level pipelines for model training and evaluation leading to reproducible models and metrics.
Experience with data analytics and wielding SQL+pandas+spark framework to both build data and metric generation pipelines, and answer critical questions about system efficacy or counterfactual treatments.
Ability to understand business requirements thoroughly and bias toward designing a simplest yet generalizable ML model / system that can accomplish the goal.
Uses a systematic approach to debug both data and system issues within ML / heuristics models.
Fluent with Python and machine learning toolkits like numpy, sklearn, pytorch and tensorflow.
Effective software engineering skills who can find answers quickly from code base and writes structured, readable, well tested and efficient code.
BS degree in Computer Science, Applied Sciences, Information Systems or other related engineering field.

Benefits

At Abnormal AI, certain roles are eligible for a bonus, restricted stock units (RSUs), and benefits. Individual compensation packages are based on factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningfeature engineeringmodel trainingdata analyticsSQLpandassparkPythonnumpysklearn

Soft Skills

problem-solvingcommunicationsystematic debuggingbusiness understandingcollaboration

Certifications

BS degree in Computer ScienceBS degree in Applied SciencesBS degree in Information Systems