Machine Learning Engineer

Hyperbound

$260K — $300K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in machine learning and model deployment
  • Proficient in fine-tuning and deploying open-source models
  • Strong understanding of quantization and distillation techniques
  • Experience with building evaluation frameworks and regression suites
  • Comfortable working closely with founders and a small team of engineers

Responsibilities

  • Build and ship models for sales call applications
  • Own the entire model lifecycle from training to deployment
  • Fine-tune models based on cost and latency requirements
  • Create benchmarks to measure model performance and improvements
  • Collaborate with engineering team to influence product direction

Benefits

  • Medical, dental, and vision insurance
  • 401k retirement plan
  • Commuter and parking benefits
  • Unlimited PTO
  • Free lunches and dinners in the office
Full Job Description
The Role

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 will fine-tune and deploy open source models where they give us more control over cost, latency, or what the model can actually do. Some of it means getting models running on-device, wherever a customer's latency or privacy requirements demand it, with all the tradeoffs around quantization and distillation that come with it. And a good amount of it means building the evaluation frameworks, benchmarks, and regression suites that tell us whether a change actually made things better, before a customer finds out for us.

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.

The Team

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.

Compensation and Benefits

Comp: $260k-$300k+ based on experience, meaningful equity

Benefits: medical, dental, vision, 401k

Commuter and parking benefits

Unlimited PTO

Free lunch and dinner in the office

The Interview Process

We move fast: an intro call, a technical conversation with the team you'd actually work with, and a final conversation with the founders. We move fast, 1-2 weeks from first conversation to offer.

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