Senior Machine Learning & Data Platform Engineer

Senior Machine Learning & Data Platform Engineer
AI, Retail Media & Real-Time Decisioning

$170,000 – $200,000 base + bonus + equity
Remote – Work from anywhere (US Based Preferred)

We’re partnering with a fast-growing, AI-driven company building real-time ad infrastructure and personalized commerce platforms. This isn’t a website – it’s core systems powering automated, high-performance advertising for major brands and retailers.

As they continue to scale, machine learning is becoming a core part of their growth strategy, and they’re looking for a Senior Machine Learning & Data Platform Engineer to build the production-grade ML infrastructure that powers intelligent decision-making across the entire platform.

This isn’t a research-focused role. It’s an opportunity to build and scale machine learning systems that directly influence millions of advertising and commerce decisions every day.

What you’d be working on:

• Designing and building the machine learning platform that powers optimisation, personalisation, and decisioning across the business
• Developing production ML models for CTR prediction, conversion prediction, ROAS optimisation, dynamic bidding, pricing, and recommendation systems
• Building scalable feature engineering pipelines and reusable feature stores for both real-time and batch inference
• Creating automated MLOps workflows covering training, deployment, monitoring, evaluation, and retraining
• Analysing large-scale commerce, advertising, and customer datasets to improve prediction accuracy and business outcomes
• Designing and running online experiments and A/B tests to measure model effectiveness
• Developing optimisation algorithms focused on objectives including CTR, CPC, CPA, ROAS, sales lift, customer acquisition, and lifetime value
• Building low-latency APIs and services that deliver real-time predictions at scale
• Driving best practices around model governance, experimentation, observability, and versioning
• Collaborating closely with Product, Engineering, Data, and Ad Operations teams to embed ML into core platform capabilities

What they’re looking for:

• 5+ years building production machine learning systems, data platforms, or large-scale analytics infrastructure
• Strong Python and SQL skills
• Proven experience deploying machine learning models into production environments
• Hands-on experience with frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, or LightGBM
• Experience building feature engineering pipelines and training datasets from large-scale event data
• Strong understanding of recommendation systems, ranking models, predictive analytics, optimisation, experimentation, and statistical modelling
• Experience with distributed data technologies such as Spark, Databricks, Kafka, or similar platforms
• Strong MLOps experience including model versioning, experiment tracking, monitoring, and automated retraining
• Familiarity with cloud-native infrastructure, Kubernetes, containers, and scalable API development

Nice to have:

• Experience within ad tech, retail media, e-commerce, or recommendation engines
• Experience optimising towards metrics such as CTR, CPC, CPA, ROAS, incrementality, or lift
• Experience with feature stores, MLflow, vector databases, or real-time inference systems
• Exposure to reinforcement learning, multi-armed bandits, learning-to-rank, or dynamic pricing algorithms
• Experience working with clickstream, purchase, POS, or identity graph data
• Experience building personalisation or recommendation systems at scale

Compensation:

• $170,000 – $190,000 base salary
• Bonus
• Equity/stock options

If you enjoy solving complex machine learning challenges, building large-scale data platforms, and creating systems that power real-world commercial outcomes, this is a fantastic opportunity to make a significant impact.

Interview Process:

• Introductory conversation with the Founder
• Technical interview with Engineering leadership
• Take-home exercise followed by a collaborative discussion focused on your approach, architecture decisions, and problem-solving process