Core offering: Online research participant recruitment and tooling
Total disclosed funding: USD 33,500,000
Recent raise: Series A, July 2023 (~£25M / ~$32M)
Company Overview
Problem Domain
Human data collection for research and AI model development/testing
Founded
2014
Industry
Research technology / Human data
Funding Track Record
Seed- 2019-12
USD 1,200,000
Series A- 2023-07-11
GBP 25,000,000 (~USD 32,000,000)
Raise reported to expand AI and research-testing offerings
Investor Signal
“Partech; Oxford Science Enterprises”
Founders
What we do
Join the Team
Data Engineer
RemoteUndisclosed, GB
Remote • Undisclosed, GB
About the Company
Company Name: Prolific
Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.
Build and maintain robust data pipelines from internal databases and SaaS applications ensuring timely and accurate data delivery.
Qualifications
Benefits
Competitive salary
Benefits
Remote working opportunities
Impactful, mission-driven culture
Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity.
Teeming tracks opportunities at over 24,000 AI startups, then works with you to find (and land) the one you'll love.
Product Designer
Part-timeNovi Sad, RS
Part-time • Novi Sad, RS
AI Researcher
Part-timeCambridge, GB
Part-time • Cambridge, GB
Machine Learning Engineer
ContractMunich, DE
Contract • Munich, DE
AI Researcher
Full-timeJerusalem
Full-time • Jerusalem
Frontend Developer
InternshipCambridge, GB
Internship • Cambridge, GB
AI Researcher
Part-timeManchester, GB
Part-time • Manchester, GB
Maintain our data warehouse with high-quality, well-structured data that supports analytics and business operations.
Design and implement scalable data infrastructure that accommodates our growing data volume and complexity.
Create and maintain APIs and microservices that expose data to applications, enabling seamless integration between data systems and business applications.
Establish processes and tools to monitor data quality, identify issues, and implement fixes promptly.
Create and maintain comprehensive documentation of data flows, models, and systems for knowledge sharing.
Work closely with analytics, research, and product teams to ensure their data needs are addressed effectively.
Implement and advocate for data engineering best practices across the organization.
Plan and execute system expansion as needed to support the company's growth and evolving analytical needs.
Continuously optimize data pipelines and warehouse performance to improve efficiency and reduce costs.
Ensure all data systems adhere to security best practices and compliance requirements.
Technical Expertise: 2+ years of hands-on experience deploying production quality code with proficiency in Python for data processing and related packages.
Data Infrastructure Knowledge: Deep understanding of SQL and analytical data warehouses (Snowflake, Redshift preferred) with proven experience implementing ETL/ELT best practices at scale.
Pipeline Management: Hands-on experience with data pipeline tools (Airflow, dbt) and strong ability to optimize for performance and reliability.
API Development Experience: Ability to design and develop robust data APIs and services that expose data to applications, bridging analytical and operational systems.
Data Modelling: Strong data modelling skills and familiarity with the Kimball methodology to create efficient, scalable data structures.
Quality Focus: Commitment to continuously improving product quality, security, and performance through rigorous testing and code reviews.
Documentation: Meticulous approach to creating and maintaining architecture and systems documentation.
Collaborative Mindset: Ability to work across teams to understand and address diverse data needs while maintaining data integrity.
Growth Orientation: Desire to continually keep up with advancements in data engineering practices and technologies.
Problem-Solving: Exceptional analytical skills to troubleshoot complex data issues and implement effective solutions.
Independence: Capability to ship medium features independently while contributing to the team's overall objectives.
