Founding Data Engineer

• You have personally built or generated the data used for a large pretraining run, from raw or synthetic sources, rather than only training on a dataset someone handed you.
• Senior and deeply hands-on. At least 5 years of strong experience, senior enough to architect the data stack and set strategy, but still coding the pipelines, running the experiments and doing the ablations yourself.
• Data as a first-class problem. A track record where the data itself is the object: curation, filtering, deduplication, quality scoring, mixtures, synthetic generation.
• Strong engineering. Deep Python, clean and typed code, async and concurrency, distributed data pipelines, TDD culture.
• Computer vision and document understanding, images to structured output, OCR, layout extraction, segmentation, geometry extraction, vectorization, raster to vector, image to scene graph, 3D or CAD.
• Graph and structured scientific data, molecular, protein or scene graphs, meshes, CAD, BIM, 3D geometry, or relational and structured world data.
• Synthetic worlds and simulation, a structured state to a simulator to a renderer to synthetic images with perfect labels, then a perception model, with an eye on the sim-to-real gap.
• Foundation models from scratch, real involvement in a large pretraining run, not only fine-tuning.
• Public evidence of data ownership, lead on a dataset, a Hugging Face release, a dataset card, a technical blog on your pipeline, or a talk on data curation or synthetic data.
• GIS and geometry, parcels, zoning layers, projections, computational geometry or constrained optimization.
• Multi-country data, heterogeneous sources, localization and varying rules.

If you are drawn to what we are building but do not meet every requirement, we still want to hear from you. Studies show that women in particular tend to apply only when they meet all of the criteria. If that is you, please do not let it hold you back. We would love to receive your application.

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