Technical Staff Member – Data Intelligence
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Job Description
Technical Staff role at Reka working with researchers and engineers to ensure data quality for ML. Building processing pipelines and optimizing data systems for model training.
Responsibilities:
- Work with model researchers to define what “good data” means for our models, including quality metrics, validation checks, and acceptance thresholds
- Explore open source datasets and create internal ones most suitable to build fundamental World Models
- Build algorithms for automated data quality assessment, data domain mixtures, and domain adaptation from synthetic to real data.
- Track datasets, metadata, provenance, and versions so experiments are reproducible and it’s clear what data went into which training and evaluation runs
- Own CI/CD and development tooling for the data stack (GitHub, Python, PyTorch), and automate repetitive workflows to reduce friction
- Track and optimize throughput, storage, and compute utilization across pipelines and related assets
Requirements:
- Strong ML and deep learning fundamentals with experience building and operating large-scale data and/or compute systems
- Comfortable moving between research questions and production engineering: you can dig into data, run analyses, and also ship reliable systems
- Demonstrated research experience with data compositions, quality, and dataset releases
- Ability to design and execute experiments with convincing unbiased outcomes
- Practical experience with distributed processing and orchestration (Spark, Ray, Airflow, or equivalents)
- Solid Python skills, and familiarity with the tooling around modern model training workflows (datasets, checkpoints, experiment tracking)
- Strong instincts around data quality: how to measure it, how to monitor it, and how to prevent regressions as things scale
- Able to work in a fast-moving environment, prioritize what matters, and communicate clearly with both researchers and engineers
- Bonus: experience with large video datasets, dataset curation for training, or building internal tooling for evaluation/analysis in ML environments
Benefits:
- Flexible work arrangements
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