Machine Learning Engineer

Location

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

About SMG

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.

Reporting to: Lead Data Engineer

Responsibilities

• 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
• Work with the DevOps team and Lead Data Engineer, and help 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

What we're looking for

• Hands-on experience taking ML models into production
• Strong software engineering fundamentals: production level Python, testing, version control and code review. You write high-quality, secure, maintainable code others can build on
• Solid grasp of the full ML lifecycle: feature engineering, model development and evaluation, and the failure modes of models in production (drift, skew, data quality)
• Comfortable owning deployment and monitoring of your own models - CI/CD for ML, and the operational instinct to keep production workloads healthy
• Exposure to at least one of forecasting, optimisation or recommendation systems, or clear aptitude to pick these up quickly
• Practical experience with modern data platforms (Snowflake, Databricks, AWS/Azure) and collaborating closely with data engineering on the data that feeds models
• Able to operate independently in a lean environment - owning delivery end to end and making sound technical calls with light direction

Desirable

• Previous experience within the Retail and Commerce Media space, or with other AdTech platforms
• Familiarity with MLOps tooling (MLflow, orchestration, model registries) and feature stores
• Familiarity with LLM systems - RAG, agentic patterns, evals, or productionising foundation-model workflows. We increasingly expect data-centric roles to be conversant here, and there is an agentic dimension to our roadmap over time
• Experience in a lean or one-deep team where you have built breadth alongside depth

Why SMG?

At SMG, we hire for the future, which is fast-moving and changing shape. Do you have the potential to help shape our business? We're looking for brilliant, diverse talent who want to grow with us - people who are curious, ambitious, and eager to learn, whether as specialists or across teams.

We value those who take ownership of their growth and bring fresh perspectives. That's why we're committed to equity, inclusion, and building a place where everyone feels empowered to grow. At SMG, it's not just about filling a role but building the future together.

Don't meet every single requirement? We still want to hear from you. If you believe you'd thrive in this role, your unique perspective might be just what we're looking for.

Benefits

• 10% discretionary bonus
• £1,800 yearly wellbeing fund (on top of your salary!)
• Free Headspace subscription
• £500 yearly "Uni Fund" for learning
• 4 extra Wellbeing Days off per year
• Summer & Winter conferences + year-round celebrations
• 4pm finishes every Friday
• Flexible and hybrid working