Founding Research Engineer - World's Audio Data

That is when the idea first emerged: what if everyone could use technology by speaking? Voice interfaces can finally digitizing businesses that currently cannot. This would increase worker productivity — which at scale lifts the entire GDP of developing countries. Globally, 1 billion people cannot read, and voice interfaces will finally give them access to digital services… often for the first time.

What we offer you

• A mission worthy of being your life’s work.
• True ownership of work.
• Top-tier teammates that are ambitious, smart and fun.
• Top percentile equity and pay that truly value your talent and opportunity cost.

What we expect from you

• Proof of exceptional talent through past accomplishments. Ask yourself: “What have I done that none of my peers did?”
• The ability to move 3× faster than your peers through extreme resourcefulness. You are comfortable solving problems with code, partnerships, fieldwork, or whatever else works.
• Strong software and data engineering skills. You can build scrapers, pipelines, and tools that process large amounts of audio.
• The judgment to determine which audio will actually improve a model—not merely collect large amounts of it.

Who are we

Hammad - CEO: ex Apple, ex Amazon, Cornell alumnus. Previously developed voice technology at Apple Siri and Amazon Alexa for about 9 years.
I created my first website at age 13 to sell wallpapers & software. This was year 2003 before even wordpress existed. It was hosted over dialup from home.
Zaid - CTO: Zaid previously designed and launched an entire AWS service: Bedrock Guardrails. Before that, at Amazon, Zaid wrote a paper on how to optimally plan and pack products in containers to minimize transportation and destination distribution cost in Amazon’s global import supply chain. This work led to $100m/year saving.

Your first 90 days

Below is one possible plan. We expect you to improve it once you understand the problem.

First 30 days: Understand which data most improves our models, audit how we currently acquire and prepare it, and ship your first new source of training-ready audio.

First 60 days: Launch several unconventional data-acquisition experiments and build the pipelines needed to filter, segment, transcribe, deduplicate, and score the audio automatically.

First 90 days: Prove at least one method that produces high-quality training data at 100× less than the market norm, then begin scaling it across multiple languages.

First year: Build a repeatable system that lets Uplift gather the data needed for a production-grade model in any language within weeks instead of multiple months.

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.