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About - We're building the future of storytelling and video editing. - We're a small team that moves rapid 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. Engineering Video is the most powerful way humans tell stories. It always has been. But creating it today is still painfully hard. Fragmented tools, steep learning curves, and workflows that get in the way of the actual creative work. We're building Cardboard to change that. Cardboard runs a real video editor in the browser, backed by a serious cloud media pipeline and an AI agent that actually understands footage. You will own how we measure and improve the quality of Cardboards AI agent. You will study real agent runs, turn important failures into evaluation cases, and measure whether changes make the product better. You will also work with product and engineering to ship those improvements. This is not a research-only, prompt-only, or QA role. Youll be working alongside a team of engineers who all care deeply about craft, including the founders. You like owning problems end to end, and youd rather ship something great this week than something perfect next quarter What you'll actually do - Define quality standards and build trusted evaluation datasets from real product usage. - Build offline and online evaluations, including automated checks and human review. - Analyze model and agent failure patterns, then improve quality through better data, evaluation methods, model selection, and, where useful, fine-tuning. - Add regression checks and release gates while tracking quality, latency, and cost. - Solve these hard problems. What we are looking for - Experience shipping and operating an LLM or agent system used by real customers. - Strong software engineering skills in TypeScript or Python, with the ability to work across both. - Experience building evaluations, datasets, experiments, or AI quality systems. - Strong product judgment and the ability to turn unclear quality problems into measurable improvements. You do not need a PhD or experience training foundation models. Evidence of building reliable AI products matters more than formal credentials or knowledge of a specific framework. Nice to have - Experience with multimodal AI, video, media, or creative software. - Experience with human labeling, model graders, or fine-tuning. - Good knowledge of experiment design and statistics. Within your first six months: - We have a trusted quality baseline for our main agent workflows. - Production failures regularly become new evaluation cases. - Important agent changes pass clear regression checks before release. - 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 technical craftsmanship and creative product 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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.