Senior Machine Learning Engineer – AdTech
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Job Description
Senior Machine Learning Engineer building predictive models and optimization pipelines for a programmatic advertising exchange. Operating scalable ML infrastructure and real-time decisioning systems.
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
- Calibrate and optimize models for individual advertisers while monitoring ranking and calibration quality
- Develop and operate scalable training orchestration pipelines across hourly, daily, and weekly schedules
- Build and maintain model registry workflows with 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
- 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
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: 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
Benefits:
- Fully remote work
- Flexible collaboration opportunities across distributed teams
- On-call participation/operational ownership opportunity
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