4 months ago

Research Engineer (Scaling Multimodal Data)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Research Engineer at World Labs focused on advancing multimodal world models by designing and building data acquisition, processing, and evaluation pipelines. You’ll determine which data moves model quality, create scalable data processing systems, and integrate data-driven fixes into model training. You’ll run experiments to prove data choices improve performance and deploy data-enrichment models, shaping the data-model-evaluation loop at scale.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Discover, evaluate, and acquire training data. You will find, evaluate, and integrate data from diverse sources. You will write scrapers, work with APIs, and make judgement calls about whether a source is worth pursuing before investing days of effort.
•Build data processing and curation systems. Design and implement data processing pipelines for filtering, deduplication, quality scoring, and curation. You will create well-abstracted systems that your teammates can pick up and extend.
•Look at the actual data constantly. You will sampling outputs, spotting distributional issues (e.g., too many screenshots, low-resolution crops, near-duplicates), and catch problems before they propagate to model training.
•Close the data → model → evaluation loop. You will diagnose model failures and trace them back to data issues, then design principled fixes to nip the problem in the bud.
•Deploy ML models for data enrichment. captioning, quality scoring, text embedding, segmentation, classification etc. You will evaluate whether these models actually help.

Pay and Benefits

Salary: USD 200,000 - 325,000 annually

Equity and Bonus:Equity

Key Requirements

•Strong software engineering fundamentals. You write well-abstracted, readable code and build reusable tools with clear interfaces.
•Deep experience with image and video data at scale. You know the data formats, the processing libraries (OpenCV, PIL, FFmpeg, PyAV), and you have hard-won intuition for what goes wrong when processing billions of samples.
•Experience with distributed computing. You’ve used frameworks like Apache Beam, Spark, Kubernetes, or Ray to process datasets that don’t fit on a single machine.
•Experience using ML models as components. You’ve built and run inference pipelines (e.g., filtering, scoring, captioning, and embedding) at billion scale, and evaluated whether they actually improved outcomes.
•A research-oriented approach to data decisions. You design experiments to validate processing choices rather than guessing. You can articulate why a filtering step exists and show evidence that it helps.

Experience:MultimodalAIVideo dataDataset engineering

Skills:Problem-solvingCommunicationCollaboration

Languages:English

Tech Stack:OpenCVPILFFmpegPyAVApache BeamSparkKubernetesRayPython

Company Brief

World Labs

Industry: AI & Machine Learning

Website

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World Labs

4 months ago

Research Engineer (Scaling Multimodal Data)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Discover, evaluate, and acquire training data. You will find, evaluate, and integrate data from diverse sources. You will write scrapers, work with APIs, and make judgement calls about whether a source is worth pursuing before investing days of effort.
•Build data processing and curation systems. Design and implement data processing pipelines for filtering, deduplication, quality scoring, and curation. You will create well-abstracted systems that your teammates can pick up and extend.
•Look at the actual data constantly. You will sampling outputs, spotting distributional issues (e.g., too many screenshots, low-resolution crops, near-duplicates), and catch problems before they propagate to model training.
•Close the data → model → evaluation loop. You will diagnose model failures and trace them back to data issues, then design principled fixes to nip the problem in the bud.
•Deploy ML models for data enrichment. captioning, quality scoring, text embedding, segmentation, classification etc. You will evaluate whether these models actually help.

Pay and Benefits

Salary: USD 200,000 - 325,000 annually

Equity and Bonus:Equity

Key Requirements

•Strong software engineering fundamentals. You write well-abstracted, readable code and build reusable tools with clear interfaces.
•Deep experience with image and video data at scale. You know the data formats, the processing libraries (OpenCV, PIL, FFmpeg, PyAV), and you have hard-won intuition for what goes wrong when processing billions of samples.
•Experience with distributed computing. You’ve used frameworks like Apache Beam, Spark, Kubernetes, or Ray to process datasets that don’t fit on a single machine.
•Experience using ML models as components. You’ve built and run inference pipelines (e.g., filtering, scoring, captioning, and embedding) at billion scale, and evaluated whether they actually improved outcomes.
•A research-oriented approach to data decisions. You design experiments to validate processing choices rather than guessing. You can articulate why a filtering step exists and show evidence that it helps.

Experience:MultimodalAIVideo dataDataset engineering

Skills:Problem-solvingCommunicationCollaboration

Languages:English

Tech Stack:OpenCVPILFFmpegPyAVApache BeamSparkKubernetesRayPython

Company Brief

World Labs

Industry: AI & Machine Learning

Website