Senior Machine Learning Engineer

Senior Machine Learning & Data Platform EngineerAI, & Real Time Systems$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: Building and scaling the machine learning platform that powers optimisation, personalisation, and decision-making across the business Developing production ML models focused on CTR prediction, conversion prediction, recommendation systems, dynamic bidding, and performance optimisation Creating scalable feature engineering pipelines and automated MLOps workflows for training, deployment, monitoring, and retraining Analysing large-scale commerce and advertising datasets to improve model performance and business outcomes Designing and running A/B tests and experiments to validate model effectiveness Building low-latency APIs and real-time prediction services at scale Partnering with Product, Engineering, and Data teams to embed machine learning 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 Experience deploying ML models into production environments Hands-on experience with Tensor Flow, PyTorch, scikit-learn, XGBoost, or similar frameworks Experience with feature engineering, large-scale datasets, and distributed technologies such as Spark, Databricks, or Kafka Strong understanding of MLOps, experimentation, predictive modelling, and recommendation systems Familiarity with cloud-native infrastructure, Kubernetes, containers, and scalable APIsNice to have: Experience within AdTech, Retail Media, E-commerce, or recommendation engines Experience working with metrics such as CTR, CPC, CPA, and ROASExposure to feature stores, MLflow, vector databases, or real-time inference systems Experience with personalisation, ranking models, reinforcement learning, or dynamic pricing