Member of Technical Staff (Research)

contextual ai•Mountain View, CA

74d

About The Position

We're revolutionizing how AI Agents work by solving AI's most critical challenge: context. The right context at the right time unlocks the accuracy and production scale that enterprises leveraging AI require. Our enterprise AI development platform sits at the intersection of breakthrough AI research and practical developer needs. Our end-to-end platform allows AI developers to easily and accurately ingest and query documents from enterprise data sources and easily embed retrieval results into their business workflows. Contextual AI was founded by the pioneers of Retrieval-Augmented Generation (RAG), the foundational technique behind the context layer, connecting foundation models to current and relevant information. Backed by the industry's most forward-thinking venture capitalists, we're not just participating in the enterprise AI revolution, we're defining it. Join us in building a future where AI doesn't just answer questions, it transforms businesses. As a Member of Technical Staff specializing in Research, you will work on cutting-edge AI research projects, contributing to advancements in Contextual RAG pipelines, including retrieval systems and language models, alignment and end-to-end training methods.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related field. Master's or PhD preferred.
Strong software engineering fundamentals and proven track record of building complex systems
Strong understanding of fundamental machine learning concepts, and practical experience in language modeling, retrieval, large-scale training, or evaluation.
Research experience (academic or industry) is a strong plus, including familiarity with experimental design, analysis, and research methodology
Strong problem-solving skills and ability to thrive in a fast-paced research environment

Responsibilities

Drive research projects in areas such as language modeling, retrieval, alignment, retrieval augmented generation, end-to-end training, evaluation, etc.
Design and implement scalable infrastructure and tooling to enable efficient and effective research and development of models and systems.
Read and stay up to date with literature, latest advancements, and best practices
Contribute to the full research pipeline from ideation to experimentation, analysis, and deployment
Publish research papers with novel contributions to state-of-the-art research