research
Posted Feb 4Research Engineer, AI Services
at Prolific
United StatesRemote
Responsibilities
- Advise and prototype bespoke solutions like evaluation methodology, rubric design, data collection approaches, quality frameworks, verifiers and validation frameworks, as well as RL environments
- Own methodology for projects - from scoping through delivery - working alongside our services team
- Build trusted relationships with customer research and engineering teams
- Collaborate with product teams to translate research insights into practical applications
- Mentor team members on advanced AI concepts and emerging research
- Track record of executing research or applied AI/ML projects with clear outcomes
Requirements
- 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. The Role
- We're seeking an AI Research Engineer to serve as Prolific's technical research partner to AI labs and enterprise customers.
- You'll work directly with customers on AI evaluation and data collection projects - advising on methodology, designing frameworks, and delivering research engagements end-to-end as part of our services team.
- Your work will directly impact how we understand, evaluate, and improve AI systems through high-quality human data.
- Serve as Prolific's technical research partner on customer engagements with AI labs and enterprise AI teams
- experience in AI/ML research, research engineering, or applied ML
- Strong knowledge of LLM evaluation methodologies, data collection design, and human feedback approaches •
- Experience designing AI-assisted or model-in-the-loop workflows - you think creatively about where AI can augment human judgment
- Strong communicator across contexts - you can hold your own with AI lab researchers, translate methodology into actionable recommendations for customers, and bring internal teams along on what matters and why.
- This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.