Research Scientist: Multimodal Retrieval & Synthetic Data

Mixedbread; United States; Remote; USD 120.000 - 190.000; Vollzeit

Benefits dieser Stelle

Competitive pay and equity

Health coverage

Conference access

Transportation support

Gym membership

Food stipends

Zusammenfassung

Mixedbread is building a full-stack retrieval research lab to rethink how search works with large models. You will design synthetic data pipelines, lead IR projects, and advance Omni to boost retrieval performance and efficiency.

The role emphasizes cross-team collaboration, data-driven improvements, and publishing results through papers and blogs. The team explores late interaction methods, multimodal retrievers, and end-to-end synthetic pipelines, aiming to push retrieval beyond single-vector

Qualifikationen

• Experience in information retrieval, embeddings, or related fields.
• A deep interest in synthetic data pipelines and data curation work.
• A degree is appreciated but not mandatory for this position.
• Proficiency in Python and ability to implement models from scratch.
• Passion for advancing search beyond single-vector similarity methods.
• Strong written and verbal communication skills.

Aufgaben

• Design synthetic data generation pipelines across modalities.
• Lead and contribute to applied research projects in Information Retrieval.
• Advance Omni, our core search platform, improving retrieval performance and efficiency.
• Create innovative feedback loop to constantly improve data generation.
• Collaborate across engineering and product teams to identify key areas of improvements in our models' performance and design the data necessary to bridge that gap.
• Publish and share results through papers, blog posts, and conferences.
• Research directions: Late Interaction, Omni-Modality, Data Refinement, End-to-End Synthetic Pipelines.

Kenntnisse

Information retrieval

Embeddings

Synthetic data pipelines

Python programming

Model implementation

Strong communication

Tools

Python

Jobbeschreibung

Mixedbread is building a full-stack retrieval research lab to rethink how search works with large models. You will design synthetic data pipelines, lead IR projects, and advance Omni to boost retrieval performance and efficiency.

The role emphasizes cross-team collaboration, data-driven improvements, and publishing results through papers and blogs. The team explores late interaction methods, multimodal retrievers, and end-to-end synthetic pipelines, aiming to push retrieval beyond single-vector