
Encord is the multimodal data management platform for AI. With Encord, AI teams can easily manage, curate, and label images, videos, audio, documents, text, and DICOM files on one unified platform while benefiting from AI-assisted speed and accuracy with human-in-the-loop workflows. Enrich petabytes of raw unstructured data into high-fidelity data for training, fine-tuning, and aligning AI models quickly and at scale. Encord is trusted by pioneering AI teams at Synthesia, Captions AI, Tractable, Stanford Medicine, Flock Safety, Protex AI, Zoopla, Philips, and many more global companies. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant.

Encord is the multimodal data management platform for AI. With Encord, AI teams can easily manage, curate, and label images, videos, audio, documents, text, and DICOM files on one unified platform while benefiting from AI-assisted speed and accuracy with human-in-the-loop workflows. Enrich petabytes of raw unstructured data into high-fidelity data for training, fine-tuning, and aligning AI models quickly and at scale. Encord is trusted by pioneering AI teams at Synthesia, Captions AI, Tractable, Stanford Medicine, Flock Safety, Protex AI, Zoopla, Philips, and many more global companies. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant.
What they do: Multimodal data management and annotation platform for AI (images, video, audio, text, DICOM, LiDAR)
Founded: 2020
Headcount (approx.): 146
Total disclosed funding: ≈ $47.15M (USD)
Compliance: SOC 2, HIPAA, GDPR
| Company |
|---|
Data curation and annotation for multimodal AI training and model evaluation
2020
Software Development
30000000
Announced to bring total disclosed funding to approximately $50M
17100000
Prior rounds reported totaling roughly $17.1M before Series B
“Backed by investors including Next47, Y Combinator, CRV, Crane Venture Partners, and Harpoon”
About The Role How do AI teams detect edge cases faster? What’s the best way for AI leaders to measure annotation pipeline performance? As our Technical Content Lead, you'll translate challenges you've likely faced yourself - dataset quality issues, annotation workflows, model performance issues - into content that resonates with AI and ML teams.
You'll bring hands-on experience from data ops, ML engineering, or data infrastructure. You understand dataset quality issues, annotation workflows, and model performance issues. That technical foundation is what enables you to create content that drives measurable business impact through brand awareness and pipeline.
We're trusted by 200+ world-class AI teams across robotics, logistics, automotive, insurance, gen AI and many others. If you want to create content at the intersection of cutting-edge AI applications and data infrastructure, this is your opportunity.
What You'll Do
About You
What We Offer