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, including censored bid-landscape modeling, contextual over-indexing, conversion propensity prediction with delayed labels, and positive-unlabelled learning.

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

• Establish exploration strategies and approaches for propensity logging.

• Calibrate and enhance models for individual advertisers while overseeing ranking and calibration quality.

• Build and manage scalable training orchestration pipelines on hourly, daily, and weekly schedules.

• Establish and maintain model registry workflows that include lineage tracking, evaluation gates, and auditable promotion processes.

• Implement isolated model instances for each advertiser, ensuring dedicated configuration and namespace separation.

• Oversee model publishing pipelines, ensuring compliance with freshness SLO and documenting fallback procedures.

• Conduct shadow deployments and champion/challenger experiments, complete with production-grade measurement logging.

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

• Ensure 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.

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

⛳️ Requirements

• Over 6 years of combined commercial experience in Data Science and Machine Learning Engineering, with at least 2 years in each field.

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

• In-depth expertise in Data Science/ML Engineering, demonstrating robust hands-on skills in the complementary domain.

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

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

• Proficient in production-level Python and possess strong SQL abilities.

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

• Practical familiarity with Kubernetes and Docker in production settings.

• Strong skills in experimentation and evaluation, including the statistical interpretation of results.

• Willingness to support operational ownership and participate in on-call activities.

• Upper-Intermediate or higher proficiency in English.

🏝️ Benefits

• Fully remote work.

• Flexible collaboration opportunities with distributed teams.

• Opportunity for on-call participation and operational ownership.