Machine Learning Engineer

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Machine Learning Engineer at Hyperbound · LumiJobs

$260k–$300k

H1B: no

Hyperbound Machine Learning Engineer at Hyperbound responsible for owning models end to end from training through production. Focus on fine-tuning and running open source models, on-device deployment, and building evaluation frameworks.

Responsibilities

You'll build and ship the models underneath Hyperbound's roleplay, scoring, and coaching products: real systems sitting on real sales calls. That means owning the full lifecycle, from training and fine-tuning through getting a model into production and keeping it working once it's there.
You'll work closely with the founders and the rest of engineering, and you'll have real input into what we build next, not just how we build it.
We're assembling a cracked team of builders, operators, and hunters. We've hired people from our competition, former co-founders, and early customers who loved the product.
The engineers you'd work alongside are builders first. Some have started their own companies. Nobody here is precious about their code, and nobody hides behind "that's not my problem," people just see something broken and fix it.
We work hard and we celebrate for real. We’re demoing features we shipped that morning, and the whiteboards get erased and rewritten constantly. You can feel it when a team actually believes in what it's building, and that's very real here. Here's the team in Bali after we raised our Series A:
At the same time, this isn't a mattress-under-your-desk startup: plenty of people here have kids, people leave to make dinner, and they come back the next morning and ship. High standards don't require burnout.
We've doubled in size since this photo was taken a few months ago:
Ownership and Equity
You own your models end to end: the training, the eval, the deployment, and everything that happens after. That cuts both ways, and when it works, that's yours too. Equity here is real, with real secondary opportunities, and we genuinely want the people who build this early to end up meaningfully rewarded for it.