ML Infrastructure Engineer

Join Zipline, a company revolutionizing the delivery of medical supplies and other essential goods. As an ML Infrastructure Engineer, you will build and operate software infrastructure that enables learning algorithms to leverage Zipline's large-scale fleet data. You will design scalable and maintainable data and ML infrastructure, own and improve data pipelines, and identify and mitigate bottlenecks in the ML development cycle. The ideal candidate will have 3+ years of professional software engineering experience, strong software engineering practices in Python, and experience with Kubernetes, cloud infrastructure, and ML frameworks.

• Construire et exploiter une infrastructure logicielle qui permet aux algorithmes d'apprentissage de tirer parti des données de flotte à grande échelle de Zipline.
• Concevoir une infrastructure de données et d'apprentissage automatique évolutive et maintenable pour les équipes d'autonomie, y compris la création, la validation, l'entraînement, l'évaluation et le déploiement des ensembles de données.
• Posséder et améliorer les pipelines de données qui alimentent le cycle de développement de l'apprentissage automatique.

• Experience building reproducible data pipelines and machine-learning pipelines
• Working knowledge of ML concepts such as datasets, training, evaluation, optimization, statistics, and modern deep learning workflows
• 3+ years of professional software engineering experience, ideally including ML infrastructure, data infrastructure, robotics, autonomy, aerospace, medical devices, or another safety-critical hardware/product environment
• Strong software engineering practices in Python in a production setting; comfort designing APIs, services, schemas, jobs, and operational workflows
• Experience with Kubernetes or other container orchestration systems for production workloads
• Experience with cloud and on-premise production infrastructure, preferably AWS, and infrastructure-as-code tools such as Terraform or CloudFormation
• Experience with PyTorch or similar ML frameworks
• Strong ownership, clear communication, and interest in building secure systems for mission-critical workflows
• Generalist mindset and willingness to work across cloud services, data platforms, developer tooling, and embedded/robotics-adjacent constraints
• Experience monitoring data statistics, system performance metrics, pipeline failures, and model/evaluation signals
• Experience with annotation systems, dataset inspection tooling, or active-learning workflows
• Experience with large-scale training systems, feature stores, data/versioned artifact stores, model registries, or experiment tracking
• Experience deploying or evaluating ML systems on real robots, autonomous vehicles, drones, or other hardware products