Senior Research Scientist, AI & Workforce Intelligence

Key Responsibilities

• Lead Jobs Engine Development: Own architecture, integrity, and expansion of AI job analysis system; build models for inference from unstructured text; ensure scientific defensibility and scalability.
• Expert on AI Implementation: Partner with product managers, engineers, and I-O psychologists to translate scientific requirements into AI-powered systems; advise on best approaches; serve as internal resource and enhance external scientific credibility.
• Drive Scientific Rigor: Apply I-O psychology principles (adverse impact, norm groups, evaluation standards) to all systems; ensure AI products meet legal and professional standards; balance speed with defensibility.

Required Skills & Capabilities

• Applied NLP and ML Engineering: Embeddings, semantic search, clustering, text classification, transformer architectures, model tuning/evaluation on messy, unstructured data.
• Occupational Data Modeling: Job titles, task statements, skills, competencies, credentials, job families, seniority levels, title normalization, job similarity, role differentiation, frameworks (O*NET, ESCO).
• Responsible AI Judgment: Fairness, explainability, auditability, bias mitigation, human review, legal/ethical considerations in AI-supported selection/development systems.
• Generative AI Evaluation: Rubric-based review, groundedness checks, error analysis, regression testing, evaluation of LLM-generated content.
• Product Judgment: Balancing accuracy, explainability, automation, expert review, user input, maintainability, uncertainty, job-specific nuance.
• Assessment and I/O Concepts: Job relatedness, criterion relationships, adverse impact, norm groups, assessment profiles, score interpretation.
• Ambiguity Ownership: Framing underspecified problems, challenging weak assumptions, learning domain constraints, driving durable solutions in a small-company environment.

Required Qualifications

• Graduate degree in I-O Psychology, Organizational Psychology, Organizational Development, or closely related field (quantitative focus preferred); doctoral degree a plus.
• Demonstrated ML engineering experience with shipped, production-grade systems.
• Experience applying modern NLP methods to behavioral, assessment-based, or labor market data.
• Track record of technically sound and legally/professionally defensible work.
• Minimum 3 years applied industry experience; 5+ years preferred.
• Experience at intersection of I-O science and algorithmic fairness strongly preferred.
• Familiarity with occupational taxonomies, vocational interests, or cognitive ability frameworks a significant plus.

Benefits

• Work-from-anywhere in the US
• A four-day work week
• Generous PTO
• Paid company shutdown (12/24–1/1)
• Medical/dental/vision insurance
• 401k with matching
• Paid new parent leave