Machine Learning Engineer, Team Leader

Poland and Middle-Eastern Europe; Poland; On-site; PLN 240,000 - 360,000; Full time

Job summary

Xebia is seeking a senior ML leader to drive end-to-end model development and deployment in a global delivery setup. You will oversee data-driven scoring projects, coordinate with backend and product teams, and shape roadmaps for scalable ML solutions.

Based in the EU region, you will contribute to cross-functional execution, work in AWS/GCP environments, and occasionally attend on-site workshops in Warsaw with a strong English-speaking team.

Qualifications

• 8+ years of experience in Machine Learning or Data Science roles in production environments.

Responsibilities

• Ensure that the team delivers high-quality ML Scoring solutions (e.g., click and conversion predictions) on schedule.

Skills

Machine Learning

People management

Model lifecycle

Python

SQL

Cloud (GCP)

Cross-functional

English

Tools

PyTorch

TensorFlow

BigQuery

Vertex AI

Job description

Xebia is a global AI-first, digital transformation, and engineering partner.With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.

You will be:

• ensuring that the team delivers high-quality ML Scoring solutions (e.g., click and conversion predictions) on schedule,
• maintaining responsibility for the team’s technical output and project milestones,
• managing the training scope for deep learning models, including feature space definition and model lifecycle standards,
• orchestrating the transition of scoring responsibilities from existing teams in cooperation with backend and product stakeholders,
• providing expert insight to future roadmaps for the ML scope.

Your profile:

• 8+ years of experience in Machine Learning or Data Science roles in production environments,
• 2+ years of direct people management experience,
• proven track record in leading end-to-end model training initiatives for business use cases,
• strong practical expertise in the full model lifecycle: data preparation, feature engineering, training, evaluation, calibration, and controlled rollout,
• hands-on proficiency in Python and SQL, with strong understanding of ML/deep learning frameworks (e.g., pytorch or tensorflow) and scalable data processing workflows,
• experience operating ML workloads in cloud environments, preferably GCP (Vertex AI, BigQuery, Cloud Storage),
• proven ability to drive cross-functional execution across ML, serving, and product domains, ensuring strategic alignment and performance visibility for stakeholders,
• strong decision-making based on business impact metrics (revenue, GMV, conversion) and experimentation results,
• very good command of English, both written and spoken, enabling effective collaboration in an international and cross-functional environment,
• openness to on-site workshops max 1-3 per quarter in Warsaw.

Work from the European Union region and a work permit are required.

Nice to have:

• experience in AdTech, marketplace ranking, or recommendation systems at scale,
• familiarity with model artifact repositories, CI/CD for ML, experiment tracking frameworks, and feature store integrations,
• exposure to online inference environments and cooperation with platform teams maintaining such services,
• experience with developing pCTR/pCVR-like models.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision