Senior Machine Learning Engineer – AdTech

This is a fully remote position, open to applicants in Germany, +1 more country.

📋 Description

• Develop and validate predictive models for bid-landscape analysis, contextual over-indexing, conversion propensity forecasting, and positive-unlabeled learning.

• Create and implement offline evaluation frameworks utilizing inverse propensity scoring and doubly-robust estimators.

• Establish exploration strategies and methods for propensity logging.

• Calibrate and enhance models tailored for individual advertisers while keeping track of ranking and calibration quality.

• Design and manage scalable training orchestration pipelines with hourly, daily, and weekly schedules.

• Construct and sustain model registry workflows featuring lineage tracking, evaluation gates, and auditable promotion processes.

• Execute isolated per-advertiser model instances with dedicated configurations and namespace separation.

• Oversee model publishing pipelines, ensure freshness SLO compliance, and manage fallback procedures.

• Conduct shadow deployments and champion/challenger experiments with production measurement logging.

• Track feature drift, prediction drift, train/serve skew, calibration decay, and label latency.

• Guarantee reproducibility through pinned environments, containerized builds, and reproducible data snapshots.

• Engage in post-launch optimization cycles and assess business impact using statistically grounded lift measurements.

• Create technical documentation and facilitate knowledge transfer to the Customer’s engineering and data teams.

⛳️ Requirements

• A minimum of 6 years of combined commercial experience in Data Science and ML Engineering, with at least 2 years in each domain.

• Proven production experience with machine learning systems that yield measurable business outcomes.

• In-depth expertise in Data Science/ML Engineering, with solid hands-on skills in the complementary area.

• Strong practical experience with gradient-boosted trees such as XGBoost, LightGBM, or CatBoost.

• Advanced understanding in at least one of the following: delayed labels, PU learning, off-policy evaluation, hierarchical estimation, or constrained optimization.

• Proficiency in production-level Python and strong SQL capabilities.

• Direct experience with ML orchestration, CI/CD pipelines, and model registry management.

• Practical familiarity with Kubernetes and Docker in production environments.

• Robust experimentation and evaluation abilities, including statistical interpretation of results.

• Upper-Intermediate or higher proficiency in English.

• Preferred: experience in AdTech, RTB, ranking, pricing, or real-time marketplace systems.

• Preferred: knowledge of contextual bandits and off-policy evaluation techniques.

• Preferred: experience with multi-tenant ML systems and data isolation strategies.

• Preferred: background in batch scoring systems with freshness SLA requirements.

• Preferred: hands-on experience with MLflow, Kubeflow, Airflow, or Argo.

• Preferred: experience with GCP services, including Vertex AI and BigQuery.

• Preferred: familiarity with Terraform and on-premises Linux infrastructure.

🏝️ Benefits

• Fully remote work environment.

• Flexible collaboration opportunities across distributed teams.

• Chance to tackle complex ML challenges that have measurable business impact.

• Contribution to a modern, high-load AdTech platform.

• Long-term partnership and opportunities for engineering ownership.