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# data engineer

- **Company:** Odyssey
- **Location:** UK
- **Contract:** Permanent contract
- **Skills:** Training Data, Computer Vision, Databases, Image Analysis, Raw Data, Data Strategy
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/member-of-technical-staff-data-engineering-odyssey-ml-8994618

## About the Role

* You're good at figuring things out. Give you an ambiguous, poorly-defined data problem and you'll work out what actually matters, then go build it. This is the trait we care about most.
 * You like the data work itself. Digging through messy multimodal data to find signal is the interesting part for you, not a means to an end.
 * You can switch between platform and infrastructure work and research-facing feature work, and you see how the two feed each other.
 * You've worked with real video and/or audio data and know what it takes to process it at scale.
 * You're comfortable working alongside researchers, translating what they need into concrete data work.
 * Roughly 1 to 5 years of relevant experience. We're open on seniority and hire for the person, not the title.

Bonus points

 * Computer vision experience, including classic CV like optical flow and feature or point tracking, not only deep models.
 * Experience with large-scale video or multimodal pre-training, data mixes, and curating data at that scale.
 * A background that mixes statistics or data science with engineering, plus hands-on video and image analysis.
 * Exposure to robotics data, human-movement data, or other emerging modalities like wearables or audio-heavy domains.
 * Audio experience specifically, which is hard to find and we value highly.

## Description

* Build and run data pipelines for large-scale multimodal datasets, from raw video, robotics, and audio through to curated, enriched training data.
 * Pull useful signals out of raw data: detecting speakers, isolating background audio, tracking points and features in video, and other signals that affect what the models learn.
 * Work on the training mix. Dedup, rebalance clusters, and dig into what's actually in the data so we can find gaps and keep it balanced.
 * Work directly with researchers, turning model requirements into a data strategy and their questions into pipeline work.
 * Improve the platform: storage, database layers, throughput, and deployment, so the whole system runs faster and more reliably.


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