Senior Machine Learning Engineer (AdTech)

Company Description Ready to shape intelligent decision-making at the scale of hundreds of millions of auction requests daily? We are looking for a Senior Machine Learning Engineer to join a dedicated Sigma Software team building advanced predictive systems for the programmatic advertising ecosystem. In this role, you will work on production-grade machine learning models, real-time optimization pipelines, and scalable infrastructure powering a live ad exchange platform. The position is fully remote with flexible collaboration opportunities across distributed teams. We at Sigma Software value engineering ownership, technical excellence, and long-term partnerships. This project offers the opportunity to work on complex ML challenges with measurable business impact while contributing to a modern, high-load AdTech platform. CUSTOMER Our Customer is a technology company operating supply-side infrastructure in the programmatic advertising ecosystem. The company manages a large-scale ad exchange processing hundreds of millions of auction requests per day and is investing in an in-house predictive decisioning capability to improve targeting, optimization, and marketplace efficiency. PROJECT You will join a Sigma Software team responsible for designing and building a predictive modeling and optimization platform integrated with a live ad exchange. The platform scores and filters supply in real time, predicts conversion probability, identifies high-performing audience contexts, builds look-alike audiences, and optimizes business objectives under explicit operational constraints. The solution includes scalable training pipelines, model orchestration, offline evaluation systems, deployment automation, and monitoring for model quality and drift detection. Key Technologies: Python, SQL, XGBoost, LightGBM, CatBoost, Kubernetes, Docker, GCP, MLflow, Kubeflow, Airflow, Argo, Terraform Job Description • 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 Customer’s 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 • 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