Machine Learning Engineer II

Abnormal Security is looking for a Machine Learning Engineer to join the Message Detection - Attack 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. That’s what makes our novel behavioral-based approach so… Abnormal. Abnormal has constantly been named as one of the top cybersecurity startups and our behavioral AI system has helped us win various cybersecurity accolades resulting in being trusted to protect more than 8% of the Fortune 1000 ( and ever growing ).

In a landscape where a single successful attack can lead to financial losses of millions of dollars, the Attack Detection team plays the central role of building an extremely high recall Detection Engine that can operate on hundreds of millions of messages at milliseconds latency. The Attack Detection team’s mission statement is to provide world-class detector efficacy to tackle changing 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, which involves modeling communication patterns to establish enterprise-wide baselines, incorporating these patterns as robust signals, and combining these signals with contextual information to create extremely precise systems. The team builds discriminative signals at various levels including message level (eg. presence of particular phrases), sender-level (eg.frequency of sender) and recipient level (eg.likelihood of receiving a safe message). These signals are then combined and utilized to train highly accurate model based as well as heuristic detectors. Additionally, to continuously adapt to new unseen attacks, the team builds out different stages in our automated model retraining pipelines including data analytics and generation stages, modeling stages, production evaluation stages as well as automated deployment stages.

This role would also have an opportunity to have a significant impact on the overall charter, direction and roadmap of the team. The Machine Learning Engineer would be involved in understanding the domain of false negatives i.e. the current and future attacks which can cause significant customer workflow disruption. They would help define the technical roadmap required to address the most pressing customer problems and simultaneously operate our detection decisioning system at an extremely high recall.

• 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

• 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

• MS degree in Computer Science, Electrical Engineering or other related engineering field
• Experience with big data, statistics and Machine Learning
• Experience with algorithms and optimization

This position is not:

• A role focused on optimizing existing machine learning models
• A research-oriented role that's two-steps removed from the product or customer
• A statistics/data science meets ML role

#LI-RT1

We’re the world’s fastest-growing cybersecurity company on a mission to make the cloud a safer place for businesses. Through applying ML, AI, and behavioral data science to the cybersecurity space, we’re leading the charge on protecting the modern workplace from all types of attacks.As a company, we’re committed to designing an employee experience that provides interesting and challenging problems to work on in a supportive, low-ego environment. We know that it’s our team that makes us successful—and we’re just getting started

Taking care of our team goes beyond the office. Our compensation and benefits philosophy is designed to put attract, motivate, and retain top talent:

Compensation targeted to the 75th percentile (for both base salary and equity)

If we want top performers to join and stay with us, we need to pay accordingly. We standardize our pay, meaning we pay for the role and level, not for any particular individual’s ability to interview or negotiate well.

Equity is an important part of our total comp strategy

When the company does well, we all do well. Equity is an important and exciting part of our total compensation strategy as a pre-IPO startup. We’re guided by the belief our team members should share in the financial success of our company and grant equity accordingly.

Unlimited PTO

All regular salaried team members enjoy unlimited PTO. We want team members to grow with us and a big part of that is making sure our team has the opportunity to rest and recharge. We also observe 12 holidays every year.

100% of healthcare premium costs covered

Taking care of our team goes beyond the office. We cover 100% of employee health care premium costs. If adding dependents, we contribute 75% of the health care premium cost, so you can be sure that you and your family are in the best possible health.

Remote-first

Operating as a remote-first company means we get to work with talented folks, no matter where they live. We prioritize a balance of deep focus time with Zoom meetings, and regular in-person events.As a fast growing startup, we continuously review, improve, and personalize our benefits offerings based on the team’s input. Don’t see something that’s important to you? Let us know

We value transparency at Abnormal, and our interview process is no exception. You can read more about our interview process here .

Abnormal Security is committed to creating a diverse work environment. All qualified applicants will receive consideration without regard to race, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status.

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