Machine Learning Engineer (up to £230k)

Dex; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Dex, partner company in London, is seeking an experienced ML Engineer to design, build, and deploy production ML models that directly influence user engagement and company earnings. You will own end-to-end ML workflows, from data pipelines to live serving, contributing to core infrastructure and scalable solutions.

You will work across data, model, and serving layers, applying statistical rigor, A/B testing, and causal methods to optimize rewards and outcomes while balancing cost and advertiser

Qualifications

• Hands-on production ML, end-to-end delivery.
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Responsibilities

• Design and ship production ML models that impact engagement and revenue.

Skills

Production ML

End-to-end ML

Statistics

A/B testing

Causal inference

Business impact focus

Education

Bachelor's in CS/Statistics

Tools

Python

PyTorch

TensorFlow

Airflow

SQL

Job description

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.

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.

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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