Role Description We're hiring our first dedicated MLOps Engineer. Our ML team runs +100 active AI projects on GCP and needs the industrialized infrastructure to match. ML and DevExp teams are moving fast on their respective fronts and they need a dedicated expert to connect the dots, align on the right practices, and help both sides deliver at their best. You'll be that person. You'll work at the heart of an AI-first environment, where shipping models fast and reliably is the core business, not a side project. Your Mission - Empower ML Engineers with the tools, infrastructure, and frameworks they need to iterate fast autonomously. - Accelerate time-to-market for production-ready ML products: seamless integration, proper service connections, access to data and resources. - Own ML CI/CD in close collaboration with the DevExp team, adapting existing frameworks to ML-specific needs, not just consuming them. - Keep ML Engineers in control of their models in production: monitor, troubleshoot, iterate, refine directly in prod, no staging/prod mirror. - Enable large-scale ML experimentation: robust, reproducible, scalable environments for both internal tests and A/B testing in production. - Deliver concrete MLOps building blocks (MLflow, Kubeflow, KubeRay...) and manage GPU infrastructure dynamically, you've seen L40 shortages mid-training, you know how to handle them. - Tackle technical debt on existing projects while laying the right foundations for what's next. - Be the technical mediator between ML and DevExp: understand both sides, propose solutions that stick. - Handle run responsibilities: on-call, post-mortems, level-1 failure analysis. Qualifications - Solid MLOps or DevOps background; projects shipped in prod matter more than years on a resume. - GCP expert: Vertex AI, GKE, GCS, BigQuery. - Full GitOps: FluxCD first, ArgoCD accepted. - Kubernetes in prod, not just in a lab. - Hands-on with real MLOps tools: MLflow, Kubeflow, KubeRay. - GPU-aware: you've managed GPU scarcity at scale during mass training runs. - Python is a must, Bash expected, Go or Rust a plus. - IaC (Terraform), containerization (Docker, Helm), observability (Prometheus, Datadog, Looker). - Fluent in French and English. Requirements - Jupyter Notebooks, broader ML/AI ecosystem. - AI Coding Assistants (Claude, Cursor, Dust). - Data pipelines (Airflow, Dataflow, Kestra). - Data lifecycle management (cost, security, encryption). - Redis clusters and infrastructure performance optimization. Benefits - Don't check every box? Apply anyway. - We're looking for the right person, not the perfect resume. If this role excites you, let's talk. - Dailymotion is an equal opportunity employer. All positions are open to people with disabilities. Need accommodations? Just let us know. Interview Process - HR Interview with Marvin (30mn) - Manager interview with Cédric (1h) - Technical Interview with 2 Architects (1h): no coding test, just a real technical conversation. - ML Interview with Brice & Samuel (1h) - Final Interview with Alan, VP Platform (1h) Additional Information - Type of Contract: Permanent - Department: Operations - Compensation: EUR 75000 - EUR 85000 yearly

• Drive major initiatives and define roadmaps for ML/AI solutions • Identify high-leverage opportunities in incentive optimization • Partner with cross-functional teams to build impactful ML applications • Lead and mentor team members to improve execution efficiency

• Frame fuzzy product problems as ML problems and pick the right approach: ranking, retrieval, classification, sequence models, LLM agents, or classic stats • Run end-to-end: data exploration, offline evaluation, prototype, online experiment, iteration • Push to the cutting edge when it matters, stay pragmatic when it doesn't • Own offline metrics (NDCG, recall@k, AUC, calibration) and tie them to online metrics (booking lift, retention, GMV) • Ship models to production with our engineering team. Our ML stack is FastAPI, PostgreSQL, BigQuery, AWS App Runner, with retrieval via FAISS and sentence-transformers, and managed LLM APIs (Claude, Gemini) • Build evaluation harnesses and monitoring so we know when models drift • Develop LLM-powered features across HostCopilot (drip campaigns, retention nudges, pricing and content suggestions) and Pal-facing surfaces (AI Concierge, semantic search, recommendations) • Partner with product to size opportunities and translate findings into roadmap decisions • Set the bar for the squad on ML rigor: offline evaluation, experiment design, and writeups

• Work to improve the open-source machine learning ecosystem • Mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM • Interact with users and contributors across the broad open-source ML ecosystem • Help foster one of the most active machine learning communities • Collaborate with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack