Senior Machine Learning Engineer (AdTech)

Sigma SoftwareAdTech, programmatic advertising

Source API remote eligibility restrictions: PolandFull-TimeSenior

Salary not disclosed

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Job Details

Languages: Upper-Intermediate or higher English level
Experience: 6+ years of combined commercial experience in Data Science and ML Engineering, including at least 2 years in each area
Required Skills: DockerPythonSQLKubernetesCI/CD

Have 6+ years of combined commercial experience in Data Science and ML Engineering, including at least 2 years in each area.
Bring strong production experience with machine learning systems that deliver measurable business impact.
Have deep expertise in Data Science or ML Engineering, with solid hands-on competence in the complementary domain.
Have practical experience with gradient-boosted trees such as XGBoost, LightGBM, or CatBoost.
Have advanced knowledge in at least one of: delayed labels, positive-unlabelled learning, off-policy evaluation, hierarchical estimation, or constrained optimization.
Have production-level Python skills and strong SQL skills.
Have hands-on experience with ML orchestration, CI/CD pipelines, and model registry management.
Have practical Kubernetes and Docker experience in production environments.
Bring experimentation and evaluation skills, including statistical interpretation of results.
Be ready to support operational ownership and participate in on-call activities.
Have Upper-Intermediate or higher English proficiency.

Build and validate predictive models, including censored bid-landscape models, contextual over-indexing, conversion propensity prediction with delayed labels, and positive-unlabelled learning.
Design offline evaluation frameworks using inverse propensity scoring and doubly-robust estimators over logged decisions.
Define exploration strategies and propensity logging approaches to support model evaluation and optimization.
Calibrate and optimize models for individual advertisers and monitor 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.
Own model publishing pipelines, freshness SLO compliance, and documented fallback procedures.
Run shadow deployments and champion/challenger experiments with production-grade measurement logging.
Monitor feature and prediction drift, train/serve skew, calibration decay, and label latency in production.
Document technical systems and support knowledge transfer to the Customer’s engineering and data teams.

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