ppAs a Senior Engineer in our ML Platform squad, you’ll drive the development of a brand-new ML Platform for the whole Engineering organization at Super Technologies. You will be enabling adoption of ML and AI across all the teams and tribes in our org, and your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily. /p pThis role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration. /p h3What you’ll be doing: /h3 ul liDesign and build scalable backend services and APIs to support ML experimentation, deployment, and monitoring. /li liRe-architect and rebuild existing data pipelines to handle real-time data processing and support production ML models. /li liDevelop tools that make it easier for ML engineers to design, test, and deploy ML pipelines. /li liEnsure platform services are robust, secure, and meet high standards for performance and reliability. /li liCollaborate with ML engineers, data scientists, and product teams to understand their needs and translate them into platform capabilities. /li liDrive adoption of ML best practices and promote reusability of tools across teams.



/li liContribute to the overall architecture and vision of the ML platform, ensuring it can scale with the company’s growth. /li liMentor junior engineers and foster a culture of knowledge sharing and continuous improvement. /li /ul h3We’re looking for someone with: /h3 ul li7+ years of professional experience as a Backend Engineer, Software Engineer, or similar role. /li liStrong programming skills in Python. /li liProven experience designing and building distributed systems and APIs at scale. /li liHands‑on experience with real‑time data processing frameworks (e.g., Kafka, Flink, Spark Streaming, Pulsar). /li liExperience working with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes). /li liStrong understanding of data modeling, storage systems, and streaming/processing architectures. /li liFamiliarity with ML tooling such as MLflow, ZenML, or Metaflow. /li liFamiliarity with ML model lifecycle management (training, deployment, monitoring). /li liExcellent collaboration and communication skills, with the ability to work cross‑functionally. /li /ul h3Bonus point for: /h3 ul liExperience building or working on ML platforms or MLOps frameworks. /li /ul /p #J-18808-Ljbffr

📌 Senior Engineer, Machine Learning Platform (Madrid)
🏢 Super
📍 Madrid