Machine Learning Engineer
Found in: Jooble CA O C2 - 1 week ago
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. 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. 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. 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.
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.
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.
~ 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
A role focused on optimizing existing machine learning models
A statistics/data science meets ML role
#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. Taking care of our team goes beyond the office. Equity is an important and exciting part of our total compensation strategy as a pre-IPO startup. 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. Inclusion Matters
All qualified applicants will receive consideration without regard to race, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status.
Privacy Policy
Learn more about Abnormal's Privacy Policy here .
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