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Job DescriptionWe 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.
What you will do
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.
Examples you may tackle
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.
Qualifications: what you bring
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.
Nice to have
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.
Why join us
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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