Senior Machine Learning Engineer – AdTech

🔥 4 minutes ago

🌐 Germany, Poland – Remote

🇩🇪 Germany – Remote 🇵🇱 Poland – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Sigma Software Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2002

💼 Consulting

🏥 Healthcare

🚘 Automotive

Consulting • Healthcare • Automotive

Sigma Software Group is a multinational company, established in 2002, that specializes in providing high-quality software development, graphic design, testing, and support services. The company focuses on delivering solutions across various industries such as automotive, telecommunications, aviation, advertising, gaming, banking, real estate, and healthcare. Sigma Software values professional growth, offers remote work opportunities worldwide, and caters to world-renowned clients like AstraZeneca, Scania, and SAS. The company emphasizes a culture of continuous education, mentorship, and flexible work environments, making it a preferred workplace for IT specialists aiming to work on complex solutions utilizing cutting-edge technologies. Sigma Software is committed to innovative solutions and engineering the future while also contributing to social causes such as charitable work in Ukraine.

📋 Description

• Build and validate predictive models for bid-landscape modeling, contextual over-indexing, conversion propensity prediction, and positive-unlabelled learning • Design and implement offline evaluation frameworks using inverse propensity scoring and doubly-robust estimators • 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 on 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, freshness SLO compliance, and fallback procedures • Run shadow deployments and champion/challenger experiments with production 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 • Upper-Intermediate or higher English level • Preferred: experience in AdTech, RTB, ranking, pricing, or real-time marketplace systems • Preferred: knowledge of contextual bandits and off-policy evaluation techniques • Preferred: experience with multi-tenant ML systems and data isolation approaches • Preferred: background in batch scoring systems with freshness SLA requirements • Preferred: hands-on experience with MLflow, Kubeflow, Airflow, or Argo • Preferred: experience with GCP services including Vertex AI and BigQuery • Preferred: familiarity with Terraform and on-prem Linux infrastructure

🏖️ Benefits

• Fully remote work • Flexible collaboration opportunities across distributed teams • Opportunity to work on complex ML challenges with measurable business impact • Contribution to a modern, high-load AdTech platform • Long-term partnership and engineering ownership opportunities