Research Engineer (Scaling Multimodal Data)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• 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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Salary

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401(k) match

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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

134% above the BLS national median for data and ml aggregate.

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

Shift type

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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

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About this role

Research Engineer (Scaling Multimodal Data) San Francisco About World Labs: We build foundational world models that can perceive, generate, reason, and interact with the 3D world - unlocking AI's full potential through spatial intelligence by transforming seeing into doing, perceiving into reasoning, and imagining into creating. We believe spatial intelligence will unlock new forms of storytelling, creativity, design, simulation, and immersive experiences across both virtual and physical worlds. We bring together a world-class team, united by a shared curiosity, passion, and deep backgrounds in technology - from AI research to systems engineering to product design - creating a tight feedback loop between our cutting-edge research and products that empower our users. About the Role: We're looking for a research engineer to help improve our in-house world models through better multimodal data. This role is about figuring out what data actually moves model quality - then building the datasets, pipelines, and experiments to prove it. The best generative models aren't just a product of model architecture and compute, they are a product of the training data. The model output reflects someone's obsession over what goes into the data, how it's processed, and what gets thrown away. We're looking for the person who does the obsessing and builds the tools to act on it at scale. This isn't a role where someone hands you a dataset and asks you to clean it. You will decide what data we need, figure out where to get it, build the processing and curation systems, and

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