Interested in this AI/ML Engineer role at Blackboard?
Apply Now →Skills & Technologies
About This Role
Senior Machine Learning Engineer, Behavioral BiometricsRemote - United States About the Role Cursive verifies authorship continuously, from first draft to submission. Instead of scanning a finished document and guessing whether a machine wrote it, Cursive analyzes the writing process itself: keystroke dynamics, revision history, and time on task. The result is evidence of how a piece of writing came to be, not a probability score.
The privacy model is the hard part and the point. Verification can run via API in the cloud or locally, on the writer's own machine, so the content of what someone writes stays private while they write it. Cursive travels with the writer across Blackboard®, Canvas, Moodle, D2L Brightspace, Google Docs, and Microsoft 365.
We enjoy moving fast, shipping new and better features, and delighting our end users. Our engineers are architects, reviewers, and product thinkers: people who know how to ship features end-to-end, leverage any and all advantages from AI, assure the quality of work, and make judgment calls when the requirements are still evolving.
On a team this small, there are no handoffs. The person who designs a feature ships it. We're looking for builders who are energized by that scope and opportunity, not overwhelmed by it.
Our team owns this space, is first to market, and is driving an industry-wide paradigm shift. Trust in our product, processes, and people are paramount. We are a team of high-integrity individuals who understand that the integrity tool market is an arms race and we intend to win through technical prowess, policy management, and by influencing the nascent narrative focused on human authentication instead of AI detection.
You will build the models that decide whether the person typing is the person we enrolled.
This is a research role first. You will own problems end to end: framing the question, designing the data collection, building and validating the model, then working with engineering to get it running inside a desktop application with a real latency and memory budget or in the cloud for scaled-server-side processing. We expect academic rigor and we expect the work to ship. What You Will Own* 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.
Required Skills and Experience* 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. We want to see that you have carried a question from hypothesis through data collection, modeling, and validation, and that you can say clearly what you found and what you did not.
- Advanced Python with fluency in the standard stack: scikit-learn, pandas, NumPy, and a deep learning framework such as PyTorch.
- Modeling range. 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. You will produce findings that product, engineering, and institutional customers rely on.
- Fluency in written and spoken English.
Preferred Skills and Experience* 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
About Blackboard Blackboard is a leading EdTech company and SaaS provider that delivers the digital environment for transformational teaching and learning. We serve thousands of institutions with the industry's most AI-advanced LMS, Blackboard Ally for accessibility, and institutional effectiveness solutions that put educators and learners at the center. We're an education company that builds technology. Learn more at blackboard.com. The expected salary range for this position is $127,000 - $160,000. The range reflects base salary only and does not include additional compensation such as company bonus or benefits. Placement within the pay range will depend on a variety of factors, such as experience, skills, internal parity, and location.
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.
This job description is not designed to contain a comprehensive listing of activities, duties, or responsibilities that are required. Nothing in this job description restricts management's right to assign or reassign duties and responsibilities at any time.
Blackboard is an equal employment opportunity/affirmative action employer and considers qualified applicants for employment without regard to race, gender, age, color, religion, national origin, marital status, disability, sexual orientation, gender identity/expression, protected military/veteran status, or any other legally protected factor.
Salary Context
This $127K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Blackboard, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($143K) sits 32% below the category median. Disclosed range: $127K to $160K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Blackboard AI Hiring
Blackboard has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.