Senior Machine Learning Engineer, Behavioral Biometrics

Job not on LinkedIn

🕒 October 9

🇺🇸 United States – Remote

💵 $127k - $160k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 0%

infoinfo

Numpy

Pandas

Python

PyTorch

Scikit-Learn

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Logo of Anthology Careers

Anthology Careers

1 - 10 employees

👥 HR Tech

🤝 B2B

☁️ SaaS

HR Tech • B2B • SaaS

Anthology Careers is a company specializing in onboarding services and HR solutions for businesses. They provide tools and support for processes such as ADP onboarding, Workbright I-9 employment verification, and time and attendance management. The company also offers specific services for Canadian hires, ensuring compliance with various employment laws and regulations. Their platform facilitates the seamless onboarding of new employees while offering assistance through FAQs and live support.

📋 Description

• Build authorship verification models as an open-set verification problem • Extract signal from keystroke telemetry, including timing distributions, digraph and trigraph latencies, pause and burst structure, editing and revision behavior, and effort over time • Optimize models for edge inference on student laptops, including latency, memory, CPU, quantization, and runtime selection • Design defensible evaluations with subject-disjoint splits and session-leakage detection • Report false accept and false reject rates and measure performance across keyboard layouts, device types, non-native typists, and writers with motor differences • Probe resistance to replay and synthetic keystroke generation • Help establish privacy and fairness practices for biometric data • Partner with data and software engineers to move models from notebook to release and remain involved after launch

🎯 Requirements

• M.S. with 3+ years of related experience, or Ph.D. in Computer Science (or related field) • Direct research experience through graduate lab work, thesis research, or publications • Experience carrying a question from hypothesis through data collection, modeling, and validation • Advanced Python with fluency in scikit-learn, pandas, NumPy, and a deep learning framework such as PyTorch • Strong with gradient-boosted trees on engineered features, including LightGBM, XGBoost, and random forests • Comfortable using sequence models when temporal structure justifies the cost • Understanding of leakage, distribution shift, small-sample effects, and evaluation setups that flatter the model • Clear technical writing • Fluency in written and spoken English

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