Senior Machine Learning Engineer (AdTech)

Requirements

• 6+ years of combined commercial experience in Data Science and ML Engineering, including at least 2 years in each area
• Strong production experience with machine learning systems delivering measurable business impact
• Deep expertise in Data Science/ML Engineering with solid hands-on competence in the complementary domain
• Strong practical experience with gradient-boosted trees such as XGBoost, LightGBM, or CatBoost
• Advanced knowledge in at least one of the following areas: delayed labels, PU learning, off-policy evaluation, hierarchical estimation, constrained optimization
• Production-level Python and strong SQL skills
• Hands-on experience with ML orchestration, CI/CD pipelines, and model registry management
• Practical experience with Kubernetes and Docker in production environments
• Strong experimentation and evaluation skills, including statistical interpretation of results
• Readiness to support operational ownership and participate in on-call activities
• Upper-Intermediate or higher English level

WILL BE A PLUS

• Experience in AdTech, RTB, ranking, pricing, or real-time marketplace systems
• Knowledge of contextual bandits and off-policy evaluation techniques
• Experience with multi-tenant ML systems and data isolation approaches
• Background in batch scoring systems with freshness SLA requirements
• Hands-on experience with MLflow, Kubeflow, Airflow, or Argo
• Experience with GCP services including Vertex AI and BigQuery
• Familiarity with Terraform and on-prem Linux infrastructure

Responsibilities

• Build and validate predictive models including censored bid-landscape modeling, contextual over-indexing, conversion propensity prediction with delayed labels, and positive-unlabelled learning
• Design and implement offline evaluation frameworks using inverse propensity scoring and doubly-robust estimators over logged decisions
• Define exploration strategies and propensity logging approaches to support reliable model evaluation and optimization
• Calibrate and optimize models for individual advertisers while independently monitoring ranking and calibration quality
• Develop and operate scalable training orchestration pipelines across hourly, daily, and weekly execution schedules
• Build and maintain model registry workflows including lineage tracking, evaluation gates, and auditable promotion processes
• Implement isolated per-advertiser model instances with dedicated configuration and namespace separation
• Own model publishing pipelines with freshness SLO compliance and documented fallback procedures
• Run shadow deployments and champion/challenger experiments with production-grade measurement logging
• Monitor feature drift, prediction drift, train/serve skew, calibration decay, and label latency in production environments
• Ensure reproducibility through pinned environments, containerized builds, and reproducible data snapshots
• Participate in post-launch optimization cycles and evaluate business impact using statistically grounded lift measurements
• Prepare technical documentation and support knowledge transfer to the Customer’s engineering and data teams