Machine Learning Engineer II
New
A
AbnormalCybersecurity
Remote - USAFull-TimeMiddle
Salary$160,700 — $231,000 USD
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Job Details
- Experience
- 3+ years experience designing, building and deploying machine learning applications; 1+ years of experience with writing stable and production level pipelines
- Required Skills
- PythonSQLMachine LearningNumpyPyTorchPandasSparkTensorflowscikit-learnNLP
Requirements
- 3+ years experience designing, building, and deploying machine learning applications in domains such as text understanding, entity recognition, NLP, computer vision, recommendation systems, or search.
- 1+ years of experience with writing stable and production-level pipelines for model training and evaluation.
- Experience with data analytics and using SQL, pandas, and Spark frameworks for data and metric generation pipelines.
- Ability to understand business requirements and design simple, generalizable ML systems.
- Systematic approach to debugging data and system issues within ML or heuristic models.
- Fluent in Python.
- Proficiency with machine learning toolkits including numpy, sklearn, pytorch, and tensorflow.
- Effective software engineering skills, including writing structured, readable, well-tested, and efficient code.
- BS degree in Computer Science, Applied Sciences, Information Systems, or a related engineering field.
Responsibilities
- Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product.
- Understand features that distinguish safe emails from email attacks and how the model stack enables their detection.
- Identify and recommend new feature groups or ML model approaches to improve detection efficacy.
- Work with infrastructure and systems engineers to productionize signals for the detection system.
- Train models on well-defined datasets to improve efficacy on specialized attacks.
- Actively monitor and improve false negative and efficacy rates for message detection categories.
- Analyze datasets to categorize capability gaps and recommend short-term feature and rule ideas.
- Contribute to building and debugging data pipelines.
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