Senior Machine Learning Engineer (AdTech)
The role involves building and validating advanced predictive models for ad bidding, including censored bid‑landscape and positive‑unlabelled learning, and designing offline evaluation frameworks with inverse propensity scoring and doubly‑robust estimators. You will also develop scalable training orchestration pipelines, model registry workflows, and per‑advertiser publishing pipelines to ensure calibrated, fresh models in production.
Skills required
About this role
• 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
Qualifications
• 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
• 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
