Member of Technical Staff (Data Intelligence)

• Work closely with model researchers, data infrastructure engineers, and cross-functional partners to ensure data is high quality and can be produced at petabyte scale in a reliable, efficient way. • Understand how data choices show up in model behaviour, build processing pipelines, and run the compute behind them to ensure models are trained on the best data possible. • Help build fundamental World Models by exploring open-source datasets and creating internal ones most suitable for the task.

• Have strong ML and deep learning fundamentals with experience building and operating large-scale data and/or compute systems. • Be comfortable moving between research questions and production engineering, able to dig into data, run analyses, and also ship reliable systems. • Have demonstrated research experience with data compositions, quality, and dataset releases. • Have the ability to design and execute experiments with convincing unbiased outcomes. • Have practical experience with distributed processing and orchestration such as Spark, Ray, Airflow, or equivalents. • Have solid Python skills and familiarity with the tooling around modern model training workflows including datasets, checkpoints, and experiment tracking. • Have strong instincts around data quality, knowing how to measure it, monitor it, and prevent regressions as things scale. • Be able to work in a fast-moving environment, prioritise what matters, and communicate clearly with both researchers and engineers.

• Have experience with large video datasets. • Bring experience with dataset curation for training. • Have experience building internal tooling for evaluation or analysis in ML environments.

• Work with model researchers to define what good data means for 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 is clear what data went into which training and evaluation runs. • Own CI/CD and development tooling for the data stack including GitHub, Python, and PyTorch, and automate repetitive workflows to reduce friction. • Track and optimise throughput, storage, and compute utilisation across pipelines and related assets.

• Work fully remotely from the US or UK. • Receive 5 weeks of paid leave. • Receive comprehensive healthcare benefits including vision and dental. • Receive visa assistance including H1B and OPT transfers for US employees. • Collaborate with top-tier engineers and researchers from organisations like Google DeepMind and Facebook AI Research. • Work in an inclusive and open culture that values diverse perspectives.

Reka's mission is to build useful multimodal artificial intelligence and use it to empower organisations and businesses. A globally distributed foundation model startup headquartered in the San Francisco Bay Area, Reka embraces a remote-first approach with top talent from around the world. The founding team and many team members have contributed to many of the breakthroughs in AI over the past decade, with backgrounds at organisations like Google DeepMind and Facebook AI Research.