Back to jobs

Senior Applied ML Engineer, Evals & Data

Reads like an AI Engineer role: building products on existing AI models.

Bengaluru · Bengaluru, Karnataka, India

Open 3 months, first seen by us 90 days ago
Pay
Pay not listed
Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Tech — team source
Education & alternatives
- You can turn "the agent feels dumb" into a number, then move it. - Strong in TypeScript or Python. No PhD needed. - Bonus: multimodal AI, video, or fine-tuning.

Tools in this posting

  • Python
  • TypeScript
Source — Tool mentions in context
- You can turn "the agent feels dumb" into a number, then move it. - Strong in TypeScript or Python. No PhD needed. - Bonus: multimodal AI, video, or fine-tuning.

Posting record

Report an error

Open 3 monthsWe first saw this posting 90 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Aug 15, 202690 days ago
Recorded sightings
53
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 90

Up longer than most. Similar jobs usually come down between day 4 and day 77.

From 51 similar postings that came down. Coming down is not always a hire.

Cardboard is hiring

1 open job

  • No new data jobs since August
Hiring record

Your next step

Check the employer’s posting for the current role and application details.

Applying through Ashby

  • No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.

Already applied? Track this application

Demand for AI Engineer roles

New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.

Weekly new AI Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 7, the most 65, and the latest week had 65.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under AI Engineer, by the work the posting describes.

AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.

Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.

An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.

Job description

View original posting ↗

About

  • We're building the future of storytelling and video editing.

  • We're a small team that moves fast and builds things we're proud of.

  • We care obsessively about taste: in design, in product, in every detail.

  • We're solving these hard problems.

  • We're backed by a Tier-1 global fund, YC, and founders of billion dollar companies.

AI Engineering

Cardboard is a video editor you can talk to. Our agent makes real edits on the timeline. When it gets one right, it feels like magic. When it gets one wrong, it costs someone their afternoon.

We're hiring a Senior Applied ML Engineer to own how good the agent is: turn real failures into evals, evals into a bar the team trusts, and that bar into shipped improvements. This isn't research-only, prompt-only, or QA.

What you'll own

  • What "good" means for our agent, and datasets built from real usage.

  • Offline and online evals: automated checks, model graders, and human review.

  • Regression gates, and quality tracked alongside latency and cost.

  • Which models the agent runs on.

Who you are

  • You've shipped an LLM or agent system that real customers used.

  • You've built evals, datasets, or AI quality systems before.

  • You can turn "the agent feels dumb" into a number, then move it.

  • Strong in TypeScript or Python. No PhD needed.

  • Bonus: multimodal AI, video, or fine-tuning.

How we work

  • You own outcomes, not tickets: decide what to build, ship it, and check it worked.

  • You talk to users and look at the data.

  • You build with AI agents every day, and you're the taste and judgment in the loop.

What success looks like

Within your first six months:

  • The team trusts a quality baseline for our main agent workflows.

  • Production failures regularly become eval cases.

  • We can show measurable improvements in key editing workflows.

What you get

You'd be surrounded by people who are absurdly good at what they do. One started coding at 11 and shipped an app with 6M+ downloads in high school. One got into CS engineering at 14 and has been working on distributed systems for 8+ years. One's an ex-founder who took a company to 1.2M users and $300M+ in transactions. That's the team. We're looking for someone who'll raise the bar on how we measure and improve AI quality. Apart from that you'd get:

  • Competitive salary and founding-team equity.

  • Unlimited tokens across every AI model. Use whatever you want, as much as you want.

  • A healthy budget for AI tools and any peripherals you need to do your best work.

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

Bengaluru, Karnataka, India

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

AI answers unavailable

We couldn’t identify enough role detail in this saved description to support an AI answer. Read the full posting