Senior Machine Learning Engineer (Remote)

We are hiring a Senior Machine Learning Engineer to build, ship, and steward production ML systems that power marketing optimisation, forecasting, and decision automation across our verticals. You will own the end‑to‑end ML lifecycle: feature pipelines, training, evaluation, model serving, monitoring, and continuous improvement. You will work closely with Data Science, Data Engineering, BI, and Program Management in a central‑squad plus vertical‑pod model. The remit values self‑sufficiency, clear communication, and dependable delivery across multiple concurrent workstreams.

• Design and operate ML pipelines in GCP: data ingestion, feature engineering in BigQuery and dbt, orchestration in Composer or Airflow, and reproducible training.
• Stand up and maintain low‑latency model services and batch scoring jobs with robust CI/CD, versioning, and rollback strategies.
• Implement monitoring for drift, data quality, and business KPIs, with alerting that prevents revenue leakage and speeds issue resolution.
• Partner with Data Science to move models from notebooks to production, including propensity, LTV and churn, and quality‑weighted bidding.
• Collaborate with Marketing, Product, and BI to integrate model outputs into campaigns, dashboards, and decision workflows, including predictive optimisation activation and SEM auditing.
• Document contracts and metrics, improve semantic layer alignment with BI, and help standardise experimentation guardrails at scale.
• Champion reliability, security, and cost stewardship for ML workloads.

• Productionise a propensity model and integrate it into media reporting and optimisation.
• Build and harden an anomaly‑detection service with Slack alerts for CPC and ingestion shifts.
• Contribute to a quality‑weighted bidding microservice and predictive optimisation workflows.
• Support first‑party data activation and identity‑aware features for paid channels.

• 5+ years in software or data engineering with 3+ years focused on ML systems in production.
• Strong Python and SQL. Proficiency with ML libraries such as scikit‑learn and one of TensorFlow or PyTorch.
• GCP experience: BigQuery, dbt, and Composer or Airflow. Experience with CI/CD and model serving frameworks or APIs.
• Practical MLOps: experiment tracking, model and data versioning, monitoring for drift and performance, and incident response.
• Experience integrating model outputs into paid marketing or product workflows at scale.
• Ability to manage several initiatives at once, set clear priorities, and communicate status and trade‑offs to technical and non‑technical partners.

• Experience with quality‑weighted bidding, uplift modelling, or reinforcement‑style policy optimisation.
• Familiarity with MMM, MTA, and experiment design in marketing contexts.
• Vertex AI or MLflow for training and deployment.
• Containerisation and service reliability skills.

• Monthly long weekends: every third Friday off.
• Wellness stipend and comprehensive parental‑leave policies.
• Remote‑first culture with flexible hours and a high‑trust environment.
• Opportunity to build ML systems aligned to an ambitious 2025‑26 roadmap inside a globally trusted brand.

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