About the job
About Contextual AI
At Contextual AI, we are at the forefront of transforming the functionality of AI Agents by addressing one of the most significant challenges in artificial intelligence: context. By providing the right context at the right moment, we enhance the precision and scalability that enterprises leveraging AI demand. Our comprehensive enterprise AI development platform bridges the gap between groundbreaking AI research and the practical needs of developers. It empowers AI developers to efficiently ingest and query documents from various enterprise data sources, seamlessly integrating retrieval results into their business processes.
Founded by the trailblazers of Retrieval-Augmented Generation (RAG), the core technique that connects foundational models to real-time, relevant information, Contextual AI is supported by visionary venture capitalists. We are not merely participants in the enterprise AI revolution; we are shaping its direction. Join us in crafting a future where AI not only answers inquiries but also revolutionizes business operations.
About the Role
As a Research-focused Member of Technical Staff, you will engage in pioneering AI research initiatives, contributing significantly to the development of Contextual RAG pipelines, including retrieval systems, language models, and alignment methodologies.
Responsibilities
- Lead research projects in language modeling, retrieval, alignment, retrieval-augmented generation, end-to-end training, and evaluation.
- Develop and implement scalable infrastructure and tools that facilitate effective research and model development.
- Continuously review and stay informed about current literature, advancements, and industry best practices.
- Participate in the entire research pipeline, from ideation and experimentation to analysis and deployment.
- Publish research findings that contribute novel insights to the field of AI.
Qualifications
- Bachelor's degree in Computer Science, Software Engineering, or a related field; Master's or PhD preferred.
- Solid software engineering fundamentals with a proven history of developing complex systems.
- Deep understanding of core machine learning principles with hands-on experience in language modeling, retrieval, large-scale training, or evaluation.
- Research experience (academic or industry) is highly advantageous, including knowledge of experimental design, analysis, and research methodologies.
- Exceptional problem-solving capabilities and the ability to excel in a dynamic research environment.
