# Research Engineer (Scaling Multimodal Data)

**Company:** [World Labs](https://hotfix.jobs/companies/world-labs)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Skills:** Opencv, Pil, Ffmpeg, Pyav, Apache Beam, Spark, Kubernetes, Ray, Pyarrow, Lance, Machine Learning, Computer Vision, Data Pipelines, Distributed Computing, Video Processing
**Posted:** 2026-04-22

> Research Engineer improves world models by curating multimodal (image/video) training datasets at scale. Builds processing pipelines, deploys ML for enrichment, and closes data-model-evaluation loop using strong engineering and research skills.

## Job Description

## What You’ll Do
- Discover, evaluate, and acquire training data. Write scrapers, work with APIs, and make judgement calls about sources.
- Build data processing and curation systems. Design pipelines for filtering, deduplication, quality scoring, and curation.
- Look at the actual data constantly. Sample outputs, spot distributional issues like too many screenshots or low-resolution crops.
- Close the data → model → evaluation loop. Diagnose model failures, trace to data issues, and design fixes.
- Deploy ML models for data enrichment (captioning, quality scoring, text embedding, segmentation, classification). Evaluate their impact.
- Make systematic, documented decisions. Ensure reproducibility for thresholds, criteria, and ratios.

## What We Require
- **Strong software engineering fundamentals**. Well-abstracted, readable code and reusable tools.
- **Deep experience with image and video data at scale**. Data formats, processing libraries (**OpenCV**, **PIL**, **FFmpeg**, **PyAV**).
- **Experience with distributed computing**. Frameworks like **Apache Beam**, **Spark**, **Kubernetes**, **Ray**.
- **Experience using ML models as components**. Build inference pipelines at billion scale and evaluate outcomes.
- Research-oriented approach to data decisions. Design experiments to validate choices.
- Familiarity with the model training lifecycle. Understand data composition effects.
- Obsession for the data-model-evaluation loop with proven track record.

## What We’d Love To See
- Familiarity with columnar/large-scale data storage (**PyArrow**, **Lance**, **Vortex**, **DeepMind Bagz**).
- Track record of discovering/integrating new data sources.
- Direct experience closing data → model quality loop.
- Strong visual intuition for data quality/diversity.
- Build tools/libraries, not just scripts.

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