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We're seeking a Research Engineer to conduct innovative research in key AI areas including evaluation methodologies (particularly those grounded in human data), alignment techniques, and synthetic data generation. You'll bridge the gap between cutting-edge AI research and practical applications, helping to translate research insights into valuable products and services. As part of our commitment to contribute to the wider AI community, you'll be involved in writing papers that advance the field. Your work will directly impact how we understand, evaluate, and improve AI systems through high-quality human data.
Job Responsibility:
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
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
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