Member of Technical Staff (Data Intelligence)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff (Data Intelligence) US, UK, Singapore, Remote In this role, you'll work closely with model researchers, data infrastructure engineers, and cross-functional partners to make sure our data is high quality and can be produced at petabyte scale in a reliable, efficient way. From understanding how data choices show up in model behavior, to building processing pipelines and running the compute behind them, you'll help ensure our models are trained on the best data we can get. What you'll do - 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 What we're looking for - 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 -

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