Data Science - AI

🇺🇸 San Francisco, California
$1K - $2K Annual
Posted 2 weeks ago
Expires June 27, 2026
Full TimeHybridData ScienceLegal Tech

Ironclad is the leading AI contracting platform that transforms agreements into assets. Contracts move faster, insights surface instantly, and agents push work forward, all with you in control. Whether you’re buying or selling, Ironclad unifies the entire process on one intelligent platform, providing leaders with the visibility they need to stay one step ahead. That’s why the world’s most transformative organizations, from Rivian to the World Health Organization and the Associated Press, trust Ironclad to accelerate their business.

We’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50 and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit or follow us on LinkedIn.

About the Role

Ironclad is accelerating its investment in AI to redefine how legal teams manage and understand contracts. As part of this effort, we are hiring an AI Evaluation Engineer to work within our AI Pillar. This role is focused on unlocking insights from our training data, designing feedback loops, and ensuring the continuous improvement of our agentic and ML or LLM-based systems through data-driven evaluation and iteration.

You’ll partner closely with AI Engineers and Product Managers to drive better model quality through systematic analysis, experimentation, and the curation of high-leverage datasets. Your work will directly impact the effectiveness of features like Smart Import, contract understanding, and agentic workflows.

What You'll Be Doing

- Analyze training and evaluation datasets to identify distributional gaps, labeling inconsistencies, and long-tail opportunitie...

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