Senior Research Scientist, AI Workforce Intelligence
Role overview
Wonderlic is seeking a Senior Research Scientist to sit at the precise intersection of I-O psychology and machine learning - someone who has spent time building real systems with real data who cares deeply about what those systems are predicting. This is not a role for an ML engineer who finds people data interesting as a side project, or for an I-O psychologist who has learned to code. We're looking for someone who has genuinely lived in both worlds and is ready to own the problems that live between them.
Wonderlic built its applied AI/ML team from scratch, developed the Jobs Engine, a first-of-its-kind machine learning system for job analysis at scale, and won the 2025 SIOP Machine Learning Competition. We're now refining and expanding that work to deliver sharper, richer job analysis insights across thousands of roles. You will own the continued improvement of our jobs engine, the system that ingests labor market data and produces job analysis at scale across thousands of roles, and serve as one of the organization's experts on how AI should be applied to both employee selection and development.
Your Impact:
As Senior Research Scientist, you will set the scientific and technical direction for how Wonderlic understands work at scale and translates assessment science into AI-powered insight. Your work will directly determine the quality, defensibility, and reach of the inferences our platform makes about jobs and people - inferences that drive hiring decisions, development plans, and coaching conversations for millions of users. You will be the person in the organization who can hold both the I-O science question and the production ML question in the same breath, and answer both.
Responsibilities
- check_circle Applied NLP and ML engineering skills: embeddings, semantic search, clustering, text classification, transformer architectures, model tuning and evaluation, all on potentially messy, unstructured data
- check_circle Occupational data modeling: job titles, task statements, skills, competencies, credentials, job families, seniority levels, title normalization, job similarity, role differentiation, and occupational frameworks such as O*NET and ESCO
- check_circle Responsible AI judgment in employment contexts: fairness, explainability, auditability, bias mitigation, human review, and legal and ethical considerations in AI-supported selection and employee development systems
- check_circle Generative AI evaluation skills: rubric-based review, groundedness checks, error analysis, regression testing, and evaluation of LLM-generated job descriptions, work-context summaries, and assessment result contextualization.
- check_circle Product judgment for applied ML systems: balancing accuracy, explainability, automation, expert review, user input, maintainability, uncertainty, and job-specific nuance.
- check_circle Working fluency with assessment and I/O concepts: job relatedness, criterion relationships, adverse impact, norm groups, assessment profiles, and score interpretation.
- check_circle Ability to own ambiguous, high-complexity problems: framing underspecified problems, challenging weak assumptions, learning domain constraints quickly, and driving durable solutions in a small-company environment.
- check_circle You came to I-O psychology because you care about work - what makes it meaningful, who thrives in it, how to measure fit. That hasn't changed.
- check_circle You have a healthy relationship with "good enough": you know that perfect is the enemy of shipped, and you have the judgment to know where the line is.
- check_circle You can hold both worlds simultaneously: what does this score mean for a real person, and how do I build the system that generates it.
- check_circle You are genuinely curious about the problems that exist for both employee selection and development, not just tolerant of them.
- check_circle You thrive in an environment that requires creativity and scrappiness: you can work comfortably in a situation where the problems are hard, the team is small, the constraints are many, and the ownership is real.
- check_circle Graduate degree in I-O Psychology, Organizational Psychology, Organizational Development, or closely related field (quantitative focus strongly preferred); doctoral degree a plus
- check_circle Demonstrated ML engineering experience with shipped, production-grade systems - not just research or coursework
- check_circle Experience applying modern NLP methods to behavioral, assessment-based, or labor market data
- check_circle Track record of work that had to be both technically sound and legally/professionally defensible
- check_circle Minimum 3 years of applied industry experience; 5+ years preferred
- check_circle Experience at the intersection of I-O science and algorithmic fairness strongly preferred
- check_circle Familiarity with occupational taxonomies, vocational interests, or cognitive ability frameworks a significant plus
- check_circle $95,000 to $110,000 based on experience and expertise.
About the company
- check_circle Work from anywhere in the United States
- check_circle Four-day work week
- check_circle Generous PTO plus a paid company shutdown from 12/24 to 1/1
- check_circle Benefits include medical, dental, vision, 401k with matching, paid new parent leave
- check_circle Scientific Precision: We apply rigorous scientific methodologies to develop assessments that accurately gauge individuals' potential and fit within various organizational contexts.
- check_circle Innovation: Our dedication to continuous improvement drives us to explore cutting-edge techniques and technologies, ensuring our assessments remain at the forefront of talent assessment.
- check_circle Impactful Solutions: By integrating I-O Psychology principles into our processes, we deliver solutions that not only meet the immediate hiring needs of organizations but also contribute to long-term success and retention.