Research Data Engineer — Multimodal and Longitudinal Datasets

The role

Build the infrastructure that preserves the meaning of a measurement from the instant a sensor records it to the moment a researcher evaluates a model. This requires reliable timestamps, provenance, participant permissions, quality checks, and reproducible processing.

The work

• Build robust pipelines for ingesting synchronized text, audio, video, physiological and behavioral data.
• Develop scalable data storage, indexing, metadata management and processing systems.
• Implement version-controlled preprocessing pipelines for filtering, segmentation, alignment, annotation and quality control.
• Design schemas linking participants, recording sessions, interview responses, sensor streams and behavioral events.
• Establish dataset lineage, provenance, consent-aware access and reproducibility standards.
• Implement automated checks for missing recordings, inconsistent timestamps, corrupted data and sensor failures.
• Build systems for handling large-scale multimodal training and evaluation datasets.
• Collaborate with research scientists to prevent leakage, confounding and accidental contamination between training and test sets.

What matters

• Strong Python and SQL skills.
• Experience with ETL pipelines, data orchestration and scalable storage.
• Familiarity with cloud or on-premise compute infrastructure.
• Understanding of time-series data, signal processing and machine-learning workflows.
• Experience with formats and tools such as Parquet, Arrow, NumPy, PyTorch, PostgreSQL or equivalent systems.
• Ability to design data systems that are auditable, reliable and appropriate for sensitive research information.

What good work looks like

A verifiable data pipeline with provenance, consistent timestamps, checks for missing data, and reproducible processing.

Why this work matters

Make the provenance and reliability of every recorded measurement defensible.

Get in touch

Send us your CV, your personal website or examples of work you have done, particularly a data pipeline or research system you built and made reproducible. Include an informal handwritten statement of purpose, with no prescribed length, telling us why this mission matters to you, what difficult work has taught you, and what you would like to take responsibility for at Walnut. This mission will demand scientific rigor, patience, independent thinking and a willingness to test ideas against reality. Write the statement entirely in your own words, without using generative AI. We will not consider statements written using AI. A photograph or scan of the statement is sufficient, and a typed version is welcome if handwriting is difficult. A reference from someone who knows your work is welcome, but not required.