Team Lead, Machine Learning

Darum lohnt es sich

Responsibilities Build and grow a high-performing ML team Align technical decisions across ML, product, and engineering Drive hands‑on problem solving across our large‑scale production system — getting models to work, shipping them, and iterating fast Own the full model development lifecycle, from research and prototyping through deployment, monitoring, and maintenance Architect a scalable ML platform that enables rapid experimentation and reliable production releases Champion the development of new analytics products within the team Design and implement geospatial analytics using state-of-the-art deep learning techniques and statistics Establish clear decision criteria for model development, ensuring alignment across the team and with product requirements Requirements Bring a strong quantitative background with an advanced degree (MSc or PhD) in computer science, engineering, mathematics, remote sensing, or similar Have advanced programming skills in Python and strong proficiency with deep learning frameworks (PyTorch/TensorFlow) and modern architectures.

Additionally, you have 2+ years of experience leading a technical team , including hiring, mentoring, and setting direction Communicate with clarity - you align stakeholders and articulate technical decisions clearly, creating shared understanding across teams Are deeply technical - you get things to work quickly, solve problems pragmatically, and own systems end‑to‑end Have prior experience in a software company, shipping productized analytics at scale, and are comfortable in a fast‑paced, high‑growth environment Bring routine in working with petabytes of data and designing effective data processing tools and workflows Work effectively with AI-assisted workflows and coding tools Are fluent in English (C1+) and German (C1+) #J-18808-Ljbffr Besides that you are familiar with MLOps tools (e.g., W&B) and high‑performance compute environments (AWS, bare‑metal) Have 5+ years of hands‑on machine learning experience with a track record of shipping production-level systems.