> Markdown version of [/jobs/ext/2016497-data-annotation-lead](https://www.wearedevelopers.com/jobs/ext/2016497-data-annotation-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Annotation Lead - **Company:** Carbon Based Technology Corporation - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $153,000.0 - $213,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Cloud Computing, Figma - **Published:** August 10, 2026 - **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/data-annotation-lead/10594524 ## About the Role * Direct experience in data annotation / labeling (required - the thing we care most about) * Ideal background in data collection, physical AI / robotics, or text annotation * Proven people and operations leadership - you've built or scaled a team or function * PM instincts: ruthless prioritization of competing requests, organized execution under ambiguity, strong stakeholder management across annotators, engineering, and clients * Clear written and verbal communication - your guidelines are the source of truth for the team * Comfort with annotation tooling and the ability to leverage AI coding tools to build internal trackers/dashboards (no formal CS background required) * Intermediate understanding of ML and how annotation quality drives model performance * A level of hardcore-ness while still treating people like people Nice to have * Previously led a data annotation team, or were a top-tier annotator yourself * Vendor / BPO management experience, ideally where quality was the primary lever * Familiarity with text annotation styles and video concepts (frame rate, keypoints, bounding boxes, temporal segments) * Experience managing distributed or offshore teams ## Description We're looking for a Data Annotation Lead to own our annotation operation end-to-end and build the team behind it. This is a lead / player-coach role with a heavy PM lean: you'll set the framework, quality bar, and tooling for annotation, turn ambiguous research requests into crisp guidelines, and scale a workforce that can pivot fast without dropping quality. You'll sit at the seam of ML/CV, product, and operations. What you'll do * Own annotation operations end-to-end - quality, throughput, and cost per delivered hour * Build and lead the in-house annotation team; own the in-house vs. partner/BPO mix and manage external vendors where used * Translate research and client requirements into clear annotation guidelines, taxonomies, and QA rubrics * Stand up the quality system: audits, inter-annotator agreement, golden sets, reviewer scorecards, escalation paths * Partner with ML/CV and product to spec and pilot new annotation task types for fast-moving experiments * Drive AI-assisted labeling (model pre-labels * human correction) to raise throughput and cut cost * Own the metrics - dashboards on quality and volume - and report the state of annotation to leadership * Stay in the weeds: annotate yourself whenever a new task type is being designed ## Related Videos - [Green Cloud Computing](https://www.wearedevelopers.com/videos/592-green-cloud-computing) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Boost Productivity with AI: Figma & Playwright MCP Workflows - Aris Markogiannakis](https://www.wearedevelopers.com/videos/1768-boost-productivity-with-ai-figma-playwright-mcp-workflows-aris-markogiannakis) - [The AI-Native Engineering Org: What’s Real, What’s Hype, What’s Next](https://www.wearedevelopers.com/videos/100004-the-ai-native-engineering-org-what-s-real-what-s-hype-what-s-next) - [Designing the Future of Human<>Agent Collaboration](https://www.wearedevelopers.com/videos/1447-designing-the-future-of-human-agent-collaboration) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)