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Open nowPosted 38 days ago

Senior Applied Machine Learning Engineer (Eval) - Bengaluru

CLANX25 open roles

Where
Bengaluru, Karnātaka, India
Work mode
On site
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Your applicationOpen nowSenior Applied Machine Learning Engineer (Eval) - BengaluruCLANX · Bengaluru, Karnātaka, India

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The clock on this job

Early applications get read.

7.8% of postings close within 7 days. Measured by our own scanner across the market. CLANX postings stay open a median of 1 days.

Share of postings closed within
  1. 1.7%1 day
  2. 3.5%3 days
  3. 7.8%7 days
  4. 14.6%14 days
  5. 34.1%30 days
This job: posted 38 days ago

CLANX median: 1 days open

The posting

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

  • Experience shipping and operating an LLM or agent system used by real customers.
  • Strong software engineering skills in TypeScript or Python, with ability to work across both.
  • Experience building evaluations, datasets, experiments, or AI quality systems.
  • Strong product judgment and ability to turn vague AI quality issues into measurable problems.
  • Ability to work across data, evaluation methods, model selection, and fine-tuning.
  • Strong ownership as a senior individual contributor.
  • Bonus: Experience with multimodal AI, video, media, or creative software.
  • Bonus: Experience with human labeling, model graders, or fine-tuning.
  • Bonus: Strong understanding of experiment design and statistics.

Responsibilities

  • Define quality standards for Cardboard’s agent.
  • Build trusted evaluation datasets from real product usage.
  • Build offline and online evaluations using automated checks, model graders, and human review.
  • Analyze real agent runs and identify recurring failure patterns.
  • Improve agent quality through better data, evaluation methods, model selection, and fine-tuning.
  • Build regression checks and release gates for important agent changes.
  • Track AI quality alongside latency and cost.
  • Partner with product and engineering teams to ship measurable improvements.

Job Details Bengaluru, India

Interview Process

  • Recruiter Screen
  • Technical Interview
  • ML & Evaluation Deep Dive
  • Product & Engineering Interview
  • Final Interview

Important Note ClanX is a recruitment partner, helping Cardboard hire Senior Applied ML Engineer, Evals & Data.

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