Senior Machine Learning Engineer (AdTech)

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 Customers 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
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

Remote Work :

Yes

Employment Type :

Full-time