Lead AI Scientist

Job not on LinkedIn

🔥 12 hours ago

🤠 Texas – Remote

🤠 Texas – Remote

💵 $140k - $160k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 0%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Pearson VUE

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1994

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Pearson VUE is a global leader in computer-based testing, providing a wide range of credentialing and certification exams for various industries. They support test-takers and test owners by offering resources, scheduling options, and accommodations to ensure equitable access to testing. Their mission is to empower candidates and enrich communities through the delivery of high-stakes exams that validate professional skills and knowledge, contributing to career advancement and industry standards.

📋 Description

• Design complete, modular AI systems spanning knowledge and data inputs, retrieval, models, agents, tools, deterministic services, human review, evaluation, and integration • Break complex problems into appropriate combinations of predictive ML, statistical methods, generative models, retrieval, graph reasoning, deterministic software, and human decision points • Build working systems and reference implementations and partner with software engineers to integrate them into Pearson’s assessment authoring system • Design architectures that evolve as models, data, domain knowledge, and evaluation evidence change • Design and implement agentic and multi-step workflows using models, tools, retrieval, state, structured outputs, and human decision points • Build knowledge-grounded systems using graph, semantic, vector, and hybrid retrieval • Co-design knowledge and graph architecture with the AI Data & Knowledge Architect • Develop AI capabilities for content creation and revision, verification, framework alignment, guidance, and analytic insights • Build and maintain evaluation benches, including expert-adjudicated datasets, challenge sets, regression suites, and protected holdout data • Partner with psychometricians and content experts to turn domain expectations into measurable criteria • Analyze failure modes, critical errors, and performance across meaningful data and task slices • Monitor deployed systems for drift and emerging failures and use findings to drive improvement • Develop predictive models, classifiers, and ranking methods where they outperform generative AI • Evaluate prompting, retrieval, structured knowledge, model selection, and model adaptation tradeoffs • Apply parameter-efficient fine-tuning and other adaptation methods when supported by evaluation evidence • Develop versioned evaluation and training assets from expert judgments and adjudicated examples • Design synthetic-data approaches with rigorous quality controls • Prototype quickly, test assumptions empirically, and pivot when evidence warrants • Build accurate, transparent, auditable AI that supports rather than supplants human expertise • Uphold responsible AI and data practices, including privacy, security, fairness, and appropriate use of student and content data • Collaborate with Technology teams, software engineers, AI Science colleagues, the AI Data & Knowledge Architect, psychometricians, learning scientists, and content development teams • Communicate findings, tradeoffs, and limitations to technical and non-technical audiences

🎯 Requirements

• Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related field, or equivalent experience • Experience designing and building complete AI or machine-learning systems end to end, not only isolated models or prompts • Strong breadth across predictive ML, statistical learning, and LLM-based, agentic, and retrieval-grounded systems, with judgment about when each fits • Demonstrated experience evaluating probabilistic systems against ground truth or expert judgment, including evaluation datasets, error analysis, and regression testing • Experience with knowledge graphs and knowledge-grounded AI sufficient to contribute to architecture decisions • Strong Python proficiency • Experience with TypeScript or ability to become productive quickly in a TypeScript codebase • Track record of innovating beyond established practice, with sound technical judgment and high autonomy • Ability to collaborate closely with engineers and domain experts, translating expertise into requirements and evaluation criteria • Preferred: Experience building knowledge and inference layers of education, learning, or assessment systems • Preferred: Experience building AI or ML systems in evidence-sensitive domains • Preferred: Hands-on experience with parameter-efficient fine-tuning or other model-adaptation methods • Preferred: Experience designing, filtering, and validating synthetic data • Preferred: Experience building expert-annotated datasets, adjudication protocols, or evaluation platforms • Preferred: Experience with graph-grounded agents, hybrid retrieval, or knowledge-intensive AI applications • Preferred: Familiarity with ontologies, semantic models, or hybrid neural-symbolic architectures • Preferred: Experience with calibration, uncertainty estimation, drift detection, or model monitoring • Preferred: Experience with TypeScript, AWS, PostgreSQL, graph data stores, Go, Python, and ML lifecycle tools such as MLflow • Preferred: Familiarity with educational measurement, psychometrics, or learning science

🏖️ Benefits

• Annual incentive program eligibility • Benefits information provided by Pearson

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