Updated 17h ago

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.

About project:

We are looking for Senior Machine Learning Engineers to join a project delivered for our international client operating in the AdTech industry. The client is building a next-generation bidding platform and is undertaking a strategic redesign of its core bidder engine to deliver more intelligent, scalable, and efficient machine learning-driven decisioning capabilities.

This role is a great opportunity to work on one of the most critical components of a large-scale programmatic advertising platform. You will contribute to the design and implementation of production-grade machine learning solutions that power real-time bidding decisions while collaborating closely with senior software engineers and platform architects. This is not a research-focused position but an engineering role centered on designing scalable ML systems that solve complex business problems in production.

The project combines machine learning, distributed systems, software engineering, and AdTech domain expertise within a collaborative international environment. You will play a key role in defining how machine learning integrates into the bidder architecture while helping shape the future of the client's decisioning platform.

You will be:

design and develop machine learning solutions supporting the redesign of the bidder platform,
collaborate with senior software engineers on bidder architecture and technical design decisions,
define how machine learning components integrate with the overall bidding platform architecture,
identify opportunities to replace rule-based decision logic with machine learning-driven approaches,
participate in architecture reviews, technical design discussions, and engineering planning activities,
optimize bidder performance through prediction, optimization, and intelligent decision-making models,
support the implementation of scalable, production-grade machine learning solutions,
collaborate with cross-functional engineering, analytics, and product teams to deliver high-quality solutions,
contribute to engineering best practices, model deployment strategies, and operational excellence,
support performance optimization, monitoring, and continuous improvement of machine learning services,
mentor engineers by sharing knowledge and promoting machine learning best practices where appropriate,
depending on seniority, take ownership of technical areas, influence architectural direction, and drive technical excellence across the platform,

Your profile:

extensive commercial experience in machine learning, applied data science, or machine learning engineering,
proven experience designing or implementing bidder systems for DSP platforms or similar AdTech solutions,
strong AdTech domain knowledge with a deep understanding of programmatic advertising ecosystems,
experience building and deploying production-grade machine learning systems,
strong understanding of prediction, optimization, ranking, and decisioning systems,
experience contributing to software architecture and system design discussions,
practical experience working within software engineering organizations and Agile development environments,
excellent communication and stakeholder collaboration skills,
strong analytical thinking and problem-solving capabilities,
ability to balance machine learning innovation with production engineering requirements,

practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.
Work from the European Union region and a work permit are required.

Nice to have:

solid software engineering background,
experience implementing or supporting MLOps practices,
experience designing distributed or large-scale machine learning systems,
experience working within real-time bidding (RTB) environments,
experience collaborating closely with backend engineering teams,
experience working with cloud-based machine learning platforms,
deep expertise in DSP architecture,
experience working with programmatic advertising platforms,
experience leading technical design and architecture initiatives,
experience mentoring software engineers, machine learning engineers, or data scientists,

experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work.
Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.