This role is with one of Dex’s trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they’re looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.
Dex is an AI recruiter agent that helps you run your job search. Tell Dex your stack, seniority, and what you want to build. We will manage your applications and surface other opportunities that are a fit.
The role
This company runs a bootstrapped rewards platform with 80 million users, profitable since day one. The models you build here directly decide what users see and what the company earns, offering a rare, direct line from your work to significant business impact. They are one of Europe’s fastest-growing companies, expanding their data and ML function in London.
You’ll join a deliberately small data and ML team, owning the entire surface end-to-end. This isn’t a role where you hand off models; you’ll design, ship, and diagnose them, with a short feedback loop measured in weeks. You’ll also help build the ML infrastructure, not just consume it, across a range of problems from reward ranking to personalization and measurement.
The work
- Design and ship production machine learning models that directly impact user engagement and company earnings.
- Translate complex mathematical business problems into concrete ML formulations.
- Build and optimize reward and payout structures, balancing user motivation with cost sustainability and advertiser value.
- Diagnose and troubleshoot underperforming reward campaigns, identifying root causes and implementing solutions.
- Contribute to the design and implementation of the core ML infrastructure, from training pipelines to live model serving.
What You Bring
- You are genuinely hands-on in production ML, having shipped models end-to-end rather than handing them off.
- You work across the full stack of a problem: data pipeline, model, and live serving.
- You have a strong grounding in statistics, including A/B testing, regression, and probability, ideally with causal inference or uplift methods.
- You are motivated as much by business impact as by technical elegance, prioritizing high-leverage solutions.
- You want to build ML infrastructure alongside your team, not just use existing systems.
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