Machine Learning Engineer

Own machine learning models end to end, from training and fine-tuning through deployment and ongoing production performance.

Responsibilities

• Take ownership of models end to end, spanning training through production
• Fine-tune and run open source models in production
• Push models on-device when a customer’s latency or privacy requirements call for it
• Build evaluation frameworks, benchmarks, and regression suites to confirm whether changes improve outcomes
• Build and ship the models that power Hyperbound’s roleplay, scoring, and coaching products
• Own the full lifecycle for getting models into production and keeping them working once deployed
• Fine-tune and deploy open source models where they provide more control over cost, latency, or what the model can do
• Work closely with the founders and engineering, with meaningful input into what you build next

Requirements

• No additional requirements were provided in the structured data

On-site / Work Style

• Office-based role in San Francisco, CA (onsite)
• In the office five days a week
• Most day-to-day time focuses on fine-tuning and running open source models in production

What We’re Building

• Hyperbound is the Revenue Activation Platform, an agentic operating system for sales
• Focus is on changing what happens next, not only recording what happened on a call
• Transforms real selling behavior into targeted roleplays, coaching, and workflow changes
• Aims to improve reps without adding management overhead

Ownership and Equity

• Full ownership of the model lifecycle: training, evaluation, deployment, and ongoing post-deployment work
• Meaningful equity with real secondary opportunities

Compensation

• USD 260,000 - 300,000 per year based on experience
• Comes with meaningful equity

Benefits

• Medical, dental, vision
• 401k
• Commuter and parking benefits
• Unlimited PTO
• Free lunch and dinner in the office

Interview Process

• Intro call
• Technical conversation with the team you would work with
• Final conversation with the founders
• Fast timeline: 1-2 weeks from first conversation to offer