Machine Learning Engineer II

This is a fully remote position, open to applicants in United States.

📋 Description

• Design and develop systems that integrate rules, models, feature engineering, and business and product inputs into an email detection solution, under the guidance of senior engineers.

• Comprehend the features that differentiate safe emails from malicious ones, and understand how our model architecture enables us to identify them.

• Identify and suggest new feature groups or machine learning model strategies that can greatly enhance detection effectiveness for a product. Collaborate with infrastructure and systems engineers to operationalize signals for the detection system.

• Write code with an emphasis on testability, clarity, edge cases, and error handling.

• Train models on clearly defined datasets to bolster model performance against specialized attacks.

• Actively monitor and enhance false negative rates and effectiveness rates for our message detection product's attack categories through feature engineering, rules, and machine learning modeling.

• Analyze false negative and false positive datasets to pinpoint capability gaps and propose short-term feature and rule enhancements to boost our detection performance.

• Contribute in other areas of the stack: building and troubleshooting data pipelines, or presenting results to customers using our tools when necessary.

⛳️ Requirements

• Over 3 years of experience in designing, building, and deploying machine learning applications in fields such as text understanding, entity recognition, natural language processing, computer vision, recommendation systems, or search.

• At least 1 year of experience in creating stable, production-level pipelines for model training and evaluation that lead to reproducible models and metrics.

• Proficient in data analytics and experienced with SQL, pandas, and the Spark framework to both construct data and metric generation pipelines, as well as to address critical questions regarding system effectiveness or counterfactual treatments.

• Capable of thoroughly understanding business requirements and inclined to design the simplest yet generalizable machine learning model or system that meets the objectives.

• Employ a systematic approach to troubleshoot both data and system issues within machine learning or heuristic models.

• Proficient in Python and familiar with machine learning toolkits such as NumPy, Scikit-learn, PyTorch, and TensorFlow.

• Possess strong software engineering skills, with the ability to quickly find answers within the codebase and create structured, readable, well-tested, and efficient code.

• Bachelor's degree in Computer Science, Applied Sciences, Information Systems, or a related engineering field.

🏝️ Benefits

• Actual compensation will be determined based on various non-discriminatory factors, including skills, experience, qualifications, and geographic location.

• In addition to base salary, this role may qualify for bonus or incentive compensation, equity, and a comprehensive benefits package.