AI Research Engineer

Summary

Join Prolific, a leader in human data infrastructure for AI development, as an AI Research Engineer. Lead independent research projects focusing on AI evaluation methodologies, alignment techniques, and synthetic data generation. You will bridge the gap between cutting-edge research and practical applications, translating insights into valuable products and services. Contribute to the academic AI community through publications and open-source contributions. Your work will directly impact how we understand, evaluate, and improve AI systems. Prolific offers a competitive salary, benefits, and remote work opportunities within an impactful, mission-driven culture.

Requirements

• 5+ years of engineering experience with significant AI/ML focus
• Demonstrated research experience through publications, open-source contributions, or impactful projects
• Strong engineering fundamentals and experience implementing AI systems in production environments
• Deep knowledge of LLM evaluation methodologies, alignment techniques, and model optimization approaches
• Experience with model fine-tuning, adapters, quantization, and distillation frameworks
• Self-motivation and ability to define and pursue research directions independently
• Excellent understanding of current challenges in AI safety, reliability, and alignment
• Strong communication skills and ability to explain complex research concepts clearly
• Passion for staying current with the rapidly evolving AI research landscape

Responsibilities

• Lead independent research projects in AI evaluation methodologies, alignment techniques, and synthetic data generation
• Design and implement novel evaluation frameworks for LLMs and agent systems that are grounded in human data
• Contribute to the academic AI community through publications and open-source contributions
• Stay at the forefront of AI research and pioneer innovative approaches to tackle pressing open challenges in the field
• Design and conduct rigorous experiments to study AI models and systems with sound methodological approaches
• Develop scalable frameworks for systematic evaluation of model behaviours and capabilities
• Create tools and frameworks that transform research insights into practical applications
• Build infrastructure to support large-scale research experiments when needed
• Apply knowledge of model fine-tuning, optimization techniques, distillation, and other ML engineering practices to support research goals
• Work closely with ML engineers, data scientists, and product teams to translate research insights into practical applications
• Mentor team members on advanced AI concepts and emerging research directions
• Communicate complex technical concepts to diverse stakeholders

Benefits

• Competitive salary
• Benefits
• Remote working