What you will do
Own the full AI feature development lifecycle, from sourcing data to quality management and relationship building at scale.
Design and deploy agentic workflows that automate multi-step processes including vendor briefing, annotation verification, and batch tracking.
Translate model evaluation signals (precision gaps, recall failures, edge case blind spots) into targeted data creation cycles.
Partner with EMs, MLEs and PMs to stay ahead of model data needs, and close the loop between evaluation findings and pipeline improvements.
Maintain clear, living documentation across data batches, annotation guidelines, and vendor SOPs.
Who you are
5+ years of technical program management/operations management with 3+ years in AI/ML data operations, LLM pipeline management, and vendor management in a tech environment.
AI-fluent: you use AI tools naturally, think in terms of what can be automated, and can hold credible conversations with both technical and non-technical stakeholders about model behavior, prompt design, and agent architecture.
Hands-on experience building or working with AI agents. Organized and detail-oriented, comfortable managing multiple vendors, deadlines, and pipelines simultaneously.
Familiar with LLM evaluation basics (precision, recall, edge cases) and can engage meaningfully with engineers on data quality.
Even better if you have
Prior experience working with NLP or AI training data pipelines.
Familiarity with annotation tools (e.g. CVAT, Label Studio, Scale AI, Surge AI, or similar).
Experience working in a startup or high-growth environment where processes are still being built.
You will thrive in this role if you
Get frustrated when things are manual that don't need to be, and you do something about it.
Can translate messy technical requirements into clear, actionable briefs without losing anything in translation.
Treat data quality as a product, not a checkbox.
Are comfortable operating in the gap between engineering and operations, and you actually enjoy being there.
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