GenAI Annotation Operations Engineer
Overview
You will own annotation workflows for NVIDIA's foundational model training, enabling high-quality data at scale. You will automate pipelines with Python, configure annotation tools, and support multi-stage model-in-the-loop processes for LLM development. Working with researchers, engineers, and PMs, you will deliver annotated datasets end-to-end. This role stands out for its fully remote flexibility within the U.S., with a preference for Pacific Time and hybrid near Santa Clara. You will be part of a team that drives GenAI innovation at a global leader in AI and accelerated computing.
What You'll Do8
- 1Configure UI configurations for annotation tasks on platforms like Scale AI, Labelbox, and SuperAnnotate.
- 2Build Python automation scripts for annotation pipelines, data processing, logging, and telemetry.
- 3Gather requirements from researchers, engineers, and annotators to design workflows.
- 4Own projects from requirement gathering to delivery of annotated datasets.
- 5Track progress, manage risks, and implement corrective actions to keep workflows on track.
- 6Troubleshoot issues in UI, pipelines, and data formatting.
- 7Document workflows and contribute to internal tools and playbooks.
- 8Support annotation for LLMs and GenAI training data.
Requirements7
- 12-5 years in data annotation operations, ML data workflows, or software/data engineering.
- 2Proficiency in Python scripting for automation and data handling (JSON, JSONL, CSV).
- 3Experience with annotation tools like Scale AI, Labelbox, or SuperAnnotate.
- 4Comfort working with AWS S3 and cloud-based storage pipelines.
- 5Strong communication skills for technical and non-technical stakeholders.
- 6Experience in multi-stage annotation workflows or model-in-the-loop systems.
- 7Familiarity with telemetry systems, GenAI/LLM training environments, or RLHF pipelines is a plus.
Salary Insight
$40 - $60k per year
Location
Required Skills
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