About KGeN
KGeN is building the Verified Distribution Protocol (VeriFi) for AI, DeFi, and Gaming — built on real users and real commerce to accelerate growth for projects across these industries.
Since its founding by global leaders in the consumer and gaming sectors, KGeN has grown to become the dominant growth engine in the Global South.
With 45.7 million users, 6.7 million monthly active users, and $64 million in annualized revenue, KGeN delivers verified user acquisition, on-chain loyalty programs, and decentralized storefronts via its POGE identity and reputation framework and a global clan network spanning more than 60 countries.
About the Role
You'll be the engineering backbone of our egocentric dataset — building the pipelines, labeling tools, and quality infrastructure that our ML researchers and ops teams depend on. When the ML Researcher identifies a gap in what the model is learning, you build the data tooling to close it. When the ops team hits an annotation bottleneck or QA failure, you diagnose it and fix it at the root.
Your output is working software and robust pipelines. The quality of your data infrastructure directly determines the quality of our dataset — which determines what humanoid robots are capable of learning.
What You'll Build
- Egocentric video ingestion and processing pipelines — multi-camera sync, IMU/depth stream handling, calibration metadata, and MCAP/ROS format support.
- Labeling tooling: hand pose annotation interfaces, object tracking review tools, action segmentation editors, and 3D grounding visualizers.
- QA and validation systems — inter-annotator agreement scoring, label drift detection, and annotation audit dashboards that surface errors before they compound into training noise.
- Semi-automated annotation pipelines integrating ML models (pose estimation, open-vocabulary detection, tracking, VLMs) to reduce manual load and improve consistency.
- Dataset versioning and release infrastructure so researchers can run clean ablations and reproduce results reliably.
- Custom internal tooling in response to specific ops and ML team requirements — scoped from a brief, shipped as production-grade code.
How You'll Work
- Partner directly with the ML Researcher to understand data gaps and build tooling that closes them — translating research requirements into concrete engineering tasks.
- Work closely with ops and annotation leads whose throughput depends on your tools — triage their blockers, diagnose root causes, ship fixes.
- Occasionally join internal syncs with ops or BD stakeholders when a data problem needs an engineer to scope it properly — extract the real requirement, then go build.
- Feed patterns and recurring issues back into the data roadmap — you're a signal source, not just an executor.
Must Have
- 3+ years building data pipelines for vision or video datasets — egocentric or robotics strongly preferred.
- Hands-on experience with labeling infrastructure: annotation tooling, QA workflows, inter-annotator agreement, or dataset quality at scale.
- Strong Python engineering — production-quality code, efficient I/O, comfortable processing large-scale video and sensor data.
- Familiarity with egocentric datasets — Ego4D, EgoExo4D, EPIC-Kitchens — and their annotation schemas.
- Practical experience integrating vision ML models into data processing stacks (pose estimation, tracking, detection).
- Comfortable working across engineering and non-engineering stakeholders — able to extract a clear technical requirement from a fuzzy ops or research ask.
Nice to Have
- Experience with MCAP, ROS bags, or LeRobot data formats.
- Understanding of hand pose representations — MANO topology, 6DoF, axis-angle.
- Frontend experience (React or similar) to ship annotation and QA UIs independently.
- Prior experience at a data company, research lab, or robotics startup working across technical and non-technical teams.
- Contributions to dataset scaling studies (Ego4D, Open X-Embodiment, EgoScale) or open-source vision/robotics projects.
