Senior Applied ML Engineer to own AI quality for Cardboard’s agentic video editor, building evaluation datasets, offline/online evals, regression checks, and feedback loops that turn production failures into measurable improvements.
Company Details
Cardboard is an AI-first video editor building agentic tools that understand user requests, work with media, and make real edits on the timeline. It is backed by a Tier-1 global fund, YC, and founders of billion-dollar companies. Website: https://www.usecardboard.com
Requirements
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Experience shipping and operating an LLM or agent system used by real customers.
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Strong software engineering skills in TypeScript or Python, with ability to work across both.
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Experience building evaluations, datasets, experiments, or AI quality systems.
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Strong product judgment and ability to turn vague AI quality issues into measurable problems.
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Ability to work across data, evaluation methods, model selection, and fine-tuning.
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Strong ownership as a senior individual contributor.
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Bonus: Experience with multimodal AI, video, media, or creative software.
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Bonus: Experience with human labeling, model graders, or fine-tuning.
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Bonus: Strong understanding of experiment design and statistics.
Responsibilities
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Define quality standards for Cardboard’s agent.
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Build trusted evaluation datasets from real product usage.
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Build offline and online evaluations using automated checks, model graders, and human review.
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Analyze real agent runs and identify recurring failure patterns.
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Improve agent quality through better data, evaluation methods, model selection, and fine-tuning.
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Build regression checks and release gates for important agent changes.
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Track AI quality alongside latency and cost.
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Partner with product and engineering teams to ship measurable improvements.
Job Details
Bengaluru, India
Interview Process
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Recruiter Screen
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Technical Interview
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ML & Evaluation Deep Dive
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Product & Engineering Interview
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Final Interview
Important Note
ClanX is a recruitment partner, helping Cardboard hire Senior Applied ML Engineer, Evals & Data.
