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Prolific operates a crowdsourced data collection platform where over 35,000 developers and researchers launch studies to gather training and evaluation data for AI systems. The tech stack reveals a mature ML/data science operation (PyTorch, TensorFlow, scikit-learn, Pandas, NumPy) paired with enterprise HR infrastructure (Workday, Greenhouse, SAP SuccessFactors), and the hiring mix—dominated by data roles (54 open positions) with minimal engineering (14)—suggests they're scaling data operations and quality assurance faster than product development, a pattern typical of platforms whose bottleneck is data labeling velocity and accuracy rather than engineering capacity.
Tech Stack 52 technologies
Core StackFigma Adobe Photoshop Adobe Illustrator JavaScript Airtable Confluence TypeScript Python AWS Datadog Pandas NumPy scikit-learn Tableau Power BI PyTorch TensorFlow Workday SAP SuccessFactors ADP Greenhouse Lever PayPal Microsoft Access Notion GCP Azure Matplotlib Seaborn Oracle HCM Cloud+22 more
What Prolific Is Building
◆Challenges
- Ensuring data quality for ai training
- Lack of high-quality human data
- Validating complex financial data
- Ensuring research outputs reflect practical realities
- Ensuring ai-generated data accuracy
- Executive-level insights
- Lack of expert finance professionals
- High-volume data entry
- Improving ai design evaluation
- High-stakes b2b ai research
▲Active Projects
- Data entry scenario simulation
- Ai training tasks
- Ai model evaluation
- Expert-led inquiry for b2b research
- Ai model training
- Ai data output evaluation
- Ai dataset auditing
- Audit ai-generated datasets
- Simulate data entry scenarios for ai testing
- Ai data quality assurance
Hiring Activity
150 roles · 70 in 30d
Department
Seniority
Company intelligence