Ubisoft Senior Machine Learning Engineer - AI Initiatives (W/M/NB) \\\| SmartRecruiters Google Chrome Microsoft Edge Apple Safari Mozilla Firefox . Senior Machine Learning Engineer - AI Initiatives (W/M/NB) Full-time Contract: Permanent Work flexibility: Office-based Company Description Ubisoft is a global leader in gaming with teams across the world creating original and memorable gaming experiences, from Assassins Creed, Rainbow Six to Just Dance and more. We believe diverse perspectives help both players and teams thrive. If youre passionate about innovation and pushing entertainment boundaries, join our journey and help us create the unknown! Job Description Join the Ubisoft AI Initiatives team and architect the future of the information lifecycle at Ubisoft. From advanced hybrid search and RAG pipelines to complex, agentic reasoning, we are engineering the tools that turn raw information into high-velocity decision-making. If you want to push the boundaries of how humans and AI collaborate to solve complex problems, this is your arena. We are seeking a highly skilled Machine Learning Engineer to design, build, evaluate, and optimize AI models and intelligent systems that power these applications. You will work at the intersection of machine learning research, large-scale data systems, and production engineering transforming cutting-edge AI capabilities into robust, scalable, real-world solutions. What you'll do Design and Optimize AI Systems: Design, fine-tune, and optimize machine learning models, including LLM-based, retrieval, and multimodal systems for production use. Build Intelligent Retrieval & Agentic Pipelines: Develop and improve RAG pipelines and agent-based workflows, including embeddings, vector search, orchestration, and evaluation. Extract Insights from Internal AI Usage: Leverage internal AI platform data (chat interactions, prompts, usage logs) to perform topic extraction, clustering, and trend analysis, transforming usage patterns into actionable product insights. Productionize and Scale Models: Deploy, monitor, and optimize ML models in cloud-native environments, ensuring scalability, reliability, and cost-efficiency. Evaluate and Improve Model Performance: Define evaluation frameworks, run experiments, and continuously improve model quality, robustness, and user impact. Collaborate Across Engineering and Product: Partner with software engineers, SREs, and product teams to integrate AI systems into scalable architectures and deliver high-impact solutions. Qualifications We are seeking a strong ML practitioner with both deep technical expertise and production-oriented engineering mindset. Must-Have Qualifications: Experience: Solid experience in machine learning engineering or applied AI, with proven experience delivering ML systems in production. Programming & Frameworks: Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent. LLMs & Modern AI: Practical experience with Large Language Models, embeddings, transformer architectures, and fine-tuning or prompt engineering techniques. RAG & Retrieval: Experience designing or implementing RAG pipelines, including vector databases, hybrid search, and query optimization strategies. Model Deployment & Cloud: Experience deploying models via APIs using Docker and cloud platforms such as AWS, GCP, or Azure. Data & System Thinking: Solid understanding of data pipelines, experimentation, evaluation metrics, and ML systems operating within distributed, scalable architectures. Nice-to-Have Qualifications: Experience building or integrating AI agents and multi-agent systems (e.g., LangGraph, CrewAI, Strands Agents). Familiarity with inference optimization techniques such as quantization, distillation, batching, or caching. Experience working with multimodal models (text, vision, audio). Knowledge of MLOps best practices, including model registries, CI/CD for ML, monitoring, and drift detection. Experience operating AI workloads in cloud-native environments (e.g., Kubernetes, serverless) and/or contributing to open-source or research initiatives. Growth Opportunities Joining our team as a Machine Learning Engineer in AI Initiatives offers the opportunity to: Build AI systems that directly impact creative workflows and decision-making across Ubisoft. Work on cutting-edge AI paradigms including agentic systems, and multimodal reasoning. Influence the evolution of AI standards and best practices within a global organization. Grow toward senior technical leadership roles or specialized AI positions. Additional Information Ubisoft's perks Profit Sharing, yearly company saving plan. 25 paid time off + 12 additional paid days off. 50% of your transportation pass is paid by the company, lunch vouchers (9/day), healthcare for you and your family, and lots of Ubisoft additional perks. Maternity leaves of 20 weeks, paternity/co-parental leaves of 7 weeks. Our office is located in Saint Mandé, (Metro line 1, Saint Mandé station). Gym available in the building. Recruitment process \\[30 minutes\\] : phone call with a Recruiter, \\[60 minutes\\] : video interview with the manager of the role and the Recruiter, \\[60 minutes\\] : final video interview with the Production Intelligence Data Associate Director Information about Ubisoft Ubisoft offers the same job opportunities to all, without any distinction of gender, ethnicity, religion, sexual orientation, social status, disability, or age. Ubisoft ensures the development of an inclusive work environment which mirrors the diversity of our gamers community. Check out this guide to help you with your application, and learn about our actions to encourage more diversity and inclusion. Check out this guide to help you with your application, and learn about our actions to encourage more diversity and inclusion. 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The role requires a strong machine learning practitioner with deep technical expertise and a production-oriented engineering mindset. Essential qualifications include solid experience in machine learning engineering or applied AI, with a proven track record of delivering ML systems in production. Proficiency in Python and hands-on experience with ML frameworks like PyTorch, TensorFlow, or JAX are necessary. Practical experience with Large Language Models, embeddings, transformer architectures, and fine-tuning or prompt engineering techniques is also required. Experience designing or implementing RAG pipelines, including vector databases and hybrid search, is essential. Furthermore, experience deploying models via APIs using Docker and cloud platforms such as AWS, GCP, or Azure is a must. A solid understanding of data pipelines, experimentation, evaluation metrics, and ML systems operating within distributed, scalable architectures is also expected.