Senior Machine Learning Engineer

Job title: Senior Machine Learning Engineer

Required skills

• Python / expert

• SQL / expert

• XGBoost / LightGBM / CatBoost / strong

• Kubernetes, Docker / strong

• MLOps / strong

• 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

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 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

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

• 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

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 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