Machine Learning Engineer

Location: Mainly remote - with minimum once a month travel to our London office

Reporting to: Lead Data Engineer

Who are we?

We’re the original pioneers in connected commerce marketing. Since 2008, we’ve been partnering with major retailers, powering global brands, and building meaningful connections with shoppers.

We simplify the mind-boggling complexity of today’s retail media landscape. We deliver impactful campaigns that connect with people where it matters. We create seamless and personalised shopping experiences. Above all, we deliver amazing results for our partners, driven by our unshakeable desire for growth. Time after time, we change the game. SMG is home to a world-class suite of commerce advertising capabilities powered by data and cutting-edge technology. We constantly push ourselves, our tech and our industry to discover innovative new ways to connect, sell and grow.

About the role

We are looking for a Machine Learning Engineer to join our Engineering Team.

The ML Engineer will turn experimental models into dependable production systems within SMG's Data function. Working across the full ML lifecycle - feature engineering, model development, deployment and monitoring, the role makes the science real: robust, tested, performant model code running reliably at scale behind SMG's Core Intelligence Services, our forecasting, optimisation and recommendation capabilities.

At SMG, this role offers the opportunity to work with rich, high-volume commerce media datasets across multiple leading retail partners, engineering the models that drive analytics, AI and commercial decision-making across modern commerce media networks. Sitting alongside data scientists and data engineers, the ML Engineer is the person the team relies on for how production ML is built and run, setting the practical engineering standard by example in a lean, fast-moving environment.

What you’ll do

• Take models from prototype to production, turning data scientists' experimental work into robust, tested, performant systems that run reliably at scale across SMG's Core Intelligence Services.
• Own feature engineering and ML-specific data quality: training-data validation, feature and label integrity, leakage and skew checks.
• Take ownership of deploying, serving and monitoring your models in production - drift and performance monitoring, retraining triggers, and the reliability of ML workloads.
• Working with the DevOps team and Lead Data Engineer, and helping shape the practical patterns for how this is done across the group.
• Shape evaluation approaches, retraining logic, and inference-cost and performance improvements, helping define, not just follow, the ML engineering standards across the Data function.
• Partner day-to-day with data scientists on modelling, and with infrastructure engineering to ensure models are built to deploy cleanly on the platform.
• Set the practical standard for how we do ML engineering, reproducibility, testing and model review, leading by example within the team.