Lead AI Scientist

Vaga não está no LinkedIn

🔥 21 horas atrás

🤠 Texas – Remoto

🤠 Texas – Remote

💵 $140.000 - $160.000 / ano

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

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Pearson VUE

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 1994

💼 Consultoria

🏥 Saúde

📦 Logística

Consulting • Healthcare • Logistics

A Pearson VUE é líder global em avaliações baseadas em computador, oferecendo uma ampla gama de exames de credenciamento e certificação para diversos setores. A empresa apoia candidatos e organizações proprietárias dos exames ao disponibilizar recursos, opções de agendamento e adaptações para garantir acesso equitativo às avaliações. Sua missão é potencializar candidatos e enriquecer comunidades por meio da aplicação de exames de alto impacto que validam competências e conhecimentos profissionais, contribuindo para o avanço na carreira e para a elevação dos padrões do setor.

Descrição

• 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

🎯 Requisitos

• 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

🏖️ Benefícios

• Annual incentive program eligibility • Benefits information provided by Pearson

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