Senior Machine Learning Engineer, Behavioral Biometrics

Blackboard is an EdTech company and SaaS provider delivering digital environments and solutions for teaching and learning. The Senior Machine Learning Engineer will build authorship verification models based on behavioral biometrics, taking research from data collection and validation through deployment in desktop applications or cloud systems. The role also focuses on privacy, fairness, evaluation, and productionizing models with engineering teams.

Responsibilities:

• Authorship verification models. This is an open-set verification problem, not classification. You will not have a fixed roster of users, enrollment data is short, and the model has to hold a decision threshold against writers it has never seen. Expect to work on similarity learning, one-class and few-shot approaches, score calibration, and drift within and across sessions
• Features and representations from keystroke telemetry. Extract signal from high-frequency, fine-grained event data: timing distributions, digraph and trigraph latencies, pause and burst structure, editing and revision behavior, effort over time. Decide what earns a place in the model and what is noise
• Inference at the edge. Models run on student laptops alongside everything else the machine is doing. You will optimize for latency, memory, and CPU, and treat quantization and runtime selection as part of the modeling problem rather than something handed off afterward
• Evaluation you can defend. Design subject-disjoint splits, hunt down session leakage, and report false accept and false reject rates rather than accuracy alone. Measure performance across keyboard layouts, device types, non-native typists, and writers with motor differences. Probe resistance to replay and synthetic keystroke generation. When a result looks too good, find out why before anyone else does
• Privacy and fairness by design. These are students, and this is biometric data. You will help decide what never leaves the device, what a fair error rate looks like across populations, and how to explain a verification result to a teacher who has to act on it
• Research into product. Partner with data and software engineers to move models from notebook to release, and stay involved once they are live

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
• Advanced Python with fluency in the standard stack: scikit-learn, pandas, NumPy, and a deep learning framework such as PyTorch
• Strong with gradient-boosted trees on engineered features (LightGBM, XGBoost, random forests), and comfortable reaching for sequence models when the temporal structure justifies the cost
• Instinct for what breaks a result. Leakage, distribution shift, small-sample effects, and evaluation setups that flatter the model
• Clear writing
• Fluency in written and spoken English
• Candidates must be legally authorized to work in the country where the role is based at the time of hire and must maintain that authorization for the duration of employment
• The company does not provide visa sponsorship or immigration support for this position
• Keystroke dynamics, behavioral biometrics, time-series or telemetry modeling, or text-revision analysis
• Open-set verification, metric learning, or one-class methods, including adjacent fields such as speaker verification and writer identification
• Model quantization, inference optimization, or deployment to desktop and browser runtimes
• Adversarial robustness or presentation attack detection
• Education technology, or work under FERPA, GDPR, or biometric privacy law