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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 future of AI development relies on a critical, indispensable component: high-quality human data. Prolific provides the world's largest and most trusted source of this data to the teams pushing the boundaries of AI technology. As an AI Engineer, you'll be building the intelligent systems that power the crucial interface between humans and models. You will be instrumental in shaping how next-generation AI systems are trained, evaluated, and aligned. This is a unique opportunity to solve novel problems and have a foundational impact on the entire AI ecosystem, working with a world-class team and the industry's leading AI labs.
Job Responsibility:
Architect & Build: Lead the design and implementation of production-grade AI systems that enhance how human feedback is collected, validated, and utilised across our platform
Solve Core Challenges: Develop sophisticated systems for fraud detection, quality assurance, and intelligent participant-to-task matching using techniques like vector search and advanced profile representation
Innovate with LLMs: Create and deploy AI agents and systems with immediate product impact - from basic automated data tagging, to advanced features like LLM-based evaluation judges, all the way to experimental ideas such as synthetic personas
Drive Technical Strategy: Provide technical leadership and mentorship on the practical application of AI/LLM techniques, defining best practices for everything from prompt engineering to fine-tuning and RAG
Ensure Reliability: Implement and own LLM observability and evaluation systems to ensure our AI features are reliable, performant, and continuously improving
Collaborate Cross-Functionally: Work closely with data scientists, platform engineers, and research teams to build cohesive, end-to-end solutions that push the boundaries of human-AI interaction
Requirements:
A strong track record of designing, building, and shipping AI-powered features that have solved real business problems
An inquisitive and experimental mindset, with a passion for tackling ambiguous problems that may not have established solutions
Deep practical knowledge of the modern AI engineering stack (e.g., RAG, vector databases, prompt engineering, fine-tuning techniques, and evaluation frameworks)
The ability to thoughtfully balance cutting-edge research with the practical constraints of production reliability and scalability
Deep expertise in one, or both of the following areas: Software Engineering & Systems Design: Expertise in Python and experience building scalable services (e.g., using FastAPI, Django). You have a strong command of system design, infrastructure, and CI/CD pipelines, and you're comfortable taking a feature from concept to deployment
Machine Learning Engineering & Research: A comprehensive background in machine learning, with deep experience in ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and a strong grasp of the underlying theory behind the models and techniques you use
Nice to have:
Experience with modern LLM orchestration frameworks (e.g., LangChain, LlamaIndex, DSPy, LangGraph)
Experience in agentic system design and tool calling (Adk, A2A, MCP, etc.)
Prior work on human-in-the-loop systems, data annotation platforms, or reinforcement learning from human feedback (RLHF)
An interest or experience in synthetic data, human-in-the-loop systems, or AI alignment
Experience building robust monitoring and observability for AI/ML systems in production
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