Research Engineer (Scaling Multimodal Data)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• 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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Verification

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Salary

$200K-$325K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

125% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

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

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

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

Read the full description at job-boards.greenhouse.io. FewerJobs shows a preview and links to the original posting.