Cardboard
Senior Applied Machine Learning Engineer (Eval)
- Eligibility
- Not stated
- Batch
- Not stated
- Experience
- Not stated
- Pay
- Not stated
How well do you fit this role?
Your resume against this posting — what you have, what’s missing, and a short plan to close the gap.
PDF or text, up to 4 MB. It stays in this browser — nothing is saved to your account.
About this role
About the role: - Cardboard is hiring a Senior Applied Machine Learning Engineer focused on evaluations and AI quality. - The role is based onsite in Bengaluru, India. - The engineer will own the quality feedback loop for Cardboard's agentic video editor. - The work involves turning failures observed in production into trusted evaluations and measurable improvements. What you'll work on: - Define quality standards for Cardboard's AI agent. - Build reliable evaluation datasets using data from real product usage. - Create offline and online evaluation systems using automated checks, model-based graders and human review. - Analyse real agent runs to identify repeated failure patterns. - Improve agent quality through better datasets, evaluation approaches, model selection and fine-tuning. - Build regression checks and release gates for important agent updates. - Monitor AI quality together with latency and cost. - Partner with product and engineering teams to ship measurable quality improvements. What we're looking for: - Candidates should have experience shipping and operating an LLM or agent system used by real customers. - Strong software engineering skills in TypeScript or Python are required, with the ability to work across both languages. - Experience building evaluations, datasets, experiments or AI quality systems is expected. - Applicants should have strong product judgment and be able to convert vague AI-quality issues into measurable problems. - Candidates should be comfortable working across data, evaluation methods, model selection and fine-tuning. - Strong ownership as a senior individual contributor is required. Good to have: - Experience with multimodal AI, video, media or creative software is considered a plus. - Experience with human labeling, model graders or fine-tuning is also beneficial. - Strong understanding of experiment design and statistics is another bonus qualification. About Cardboard: - Cardboard is an AI-first video editor building agentic tools that interpret user requests, work with media and make actual edits on a timeline. - The company is backed by a Tier-1 global fund, YC and founders of billion-dollar companies. Interview process: - The process includes Recruiter Screen, Technical Interview, ML & Evaluation Deep Dive, Product & Engineering Interview, and Final Interview. Note: ClanX is the recruitment partner helping Cardboard hire for this role.