Team Leader, Machine Learning (ML Scoring / AdTech)

• DurationLong-term engagement (Start: 01.06.2026 / immediate)
• DimensionFull time
• LocationRemote / Warsaw (project meetings in Warsaw)
• LevelLead / Manager (8+ yrs ML, 2+ yrs leadership)

Project description

Ensure the engineering team delivers high-quality ML Scoring solutions (e.g. click and conversion predictions) for Sponsored Offers on schedule, maintaining full ownership of technical output and project milestones. Manage the training scope for deep learning models, feature space definition, and model lifecycle standards.

Key responsibilities

• Lead the ML and Data Science engineering team, ensuring timely delivery of ML Scoring solutions.
• Manage deep learning model training, feature space design, and lifecycle governance.
• Orchestrate the handover of scoring responsibilities from existing teams in cooperation with backend and product teams.
• Provide expert contributions to future roadmaps for the ML scope in Sponsored Offers.
• Drive cross-functional execution across ML, serving, and product domains with Director-level reporting.

Required qualifications

• 8+ years of experience in Machine Learning or Data Science production environments.
• 2+ years of direct people management and team leadership experience.
• Proven track record in leading end-to-end model training initiatives for business use cases.
• Deep practical expertise in the full model lifecycle: data prep, feature engineering, training, evaluation, calibration, and rollout.
• Hands-on proficiency in Python and SQL, with strong understanding of PyTorch / TensorFlow and scalable data processing (Dask / Spark).
• Experience operating ML workloads in cloud environments, preferably GCP (Vertex AI, BigQuery, Cloud Storage).
• Decision-making based on business impact metrics (revenue, GMV, conversion) and experimentation results.
• Fluency in English (min. B2 level).

Nice to have

• Experience in AdTech, marketplace ranking, or large-scale recommendation systems.
• Familiarity with model artifact repositories, MLOps CI/CD, experiment tracking (MLflow), and Feature Stores.
• Exposure to low-latency online inference environments.
• Experience with developing pCTR / pCVR models.

Language requirements

English (B2+ level)