Senior Applied Machine Learning Engineer (Eval) - Bengaluru
Bengaluru · Senior
Applicants who checked fit first are 3.1× more likely to hear back
Your score for this role already exists
ASAI compared this JD against 41 signals - skills, seniority, domain, stack overlap etc. Add a resume and it unlocks in about 30 seconds.
No credit card · 1 tap with Google
What we know about this role
MEDIUM
ClanX is reviewing applications at a steady pace. Expect a standard response time as they evaluate the current pool.
First 72 hours
Window passed - posted 40d agoEarly applicants get seen before the pile builds.
Not a repost
The first time we've seen this listing - it hasn't been closed and reopened.
Skills required
4 listedYou almost certainly match several of these already. Unlock your skill map to see the matches, the gaps, and what to fix first.
Job description
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.
Free · no signup
Get tomorrow's jobs before you have to search
Daily job drops, skill trends and free resources - posted straight to the group. Leave any time.
No spam. Just jobs and resources.
Why people use ASAI
Someone shared one job with you. ASAI keeps finding the rest.
Scored, not searched. Every role ranked against your actual profile.
Alerts as often as hourly. Reach new roles while the pile is still small.
Skill gaps, spelled out. See exactly which requirements you don't meet yet.
Verified jobs, only. Say no to ghost jobs. Your time deserves respect.
More Machine Learning Engineer roles in Bengaluru
See allKeep browsing