Senior ML Engineer (Stockholm)

Requirements

• experience deploying or operating Federated Learning systems in production environments,
• hands-on experience with on-device machine learning technologies such as TensorFlow Lite, ONNX Runtime Mobile, or Core M,
• experience building machine learning solutions for mobile applications,
• experience in messaging, spam detection, fraud detection, trust & safety, or similar domains,
• familiarity with the challenges of running ML workloads on mobile devices,
• experience with MLOps, model monitoring, and automated training/deployment pipelines.
• Recruitment Process:
• CV review - HR call - Interview - Client Interview - Decision

Benefits

Additional Information

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: leading the technical evaluation and implementation of Federated Learning (FL) initiatives, work closely with Data Science, Android, and Backend teams to design and validate end-to-end FL workflows, defining and executing experimentation plans to assess the effectiveness of FL for use cases, developing and optimizing language models and on-device training pipelines for privacy-preserving machine learning, establish model evaluation frameworks, success metrics, and validation strategies for FL-based systems, identifying technical risks, assumptions, and limitations, and provide recommendations on architecture and future direction, helping shape the roadmap for scaling FL from experimentation to production-ready systems. Your profile: working from the office in Stockholm (mandatory), 5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or related fields, hands-on experience with Federated Learning frameworks such as TensorFlow Federated, Flower, FedML, OpenFL, or equivalent, strong understanding of distributed machine learning, model training, and model evaluation techniques, experience working with NLP, language models, embeddings, or text classification systems, hands-on experience deploying ML models on mobile devices (e.g., TensorFlow Lite, Core ML, ONNX Runtime Mobile), strong knowledge of machine learning frameworks such as TensorFlow and PyTorch, experience designing and executing ML experiments, analyzing results, and driving data-driven decisions, familiarity with privacy-preserving machine learning concepts and challenges, ability to work across multiple teams and communicate complex technical concepts to both technical and non-technical stakeholders, strong problem-solving skills and ability to operate in an exploratory research and PoC environment.

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