Machine Learning Engineer Intern

Join our Offline Infrastructure team as a Machine Learning Engineer Intern. This role is perfect for those passionate about large-scale systems and applying research-driven thinking to real-world machine learning problems. You will help build and evolve the infrastructure that powers training data generation, ML workflows, and distributed model training. You will work closely with experienced engineers and researchers, contributing to systems that ensure our ML pipelines are reliable, scalable, and efficient.

- Contribuer à la construction et à l'évolution de l'infrastructure qui alimente la génération de données d'entraînement, les flux de travail ML et l'entraînement de modèles distribués. - Participer à l'amélioration de la reproductibilité et de la fiabilité grâce à la validation, à la surveillance et aux tests des ensembles de données. - Collaborer avec des ingénieurs en ML pour soutenir l'expérimentation et l'itération des modèles.

- Bachelor’s degree in Computer Science, Machine Learning, Systems, or a related field - Strong foundation in machine learning systems, distributed systems, or large-scale data processing (through research or projects) - Strong problem-solving skills and ability to translate research ideas into practical systems - Experience (academic or applied) with data pipelines, model training workflows, or large datasets - Interest in building scalable, reliable infrastructure for machine learning - Experience with workflow orchestration systems (Airflow, Flyte, etc.) - Publications or research in ML systems, distributed systems, or related areas - Nice to Have - Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and/or distributed systems (e.g., Ray, Spark) - Experience with Python and working with data-intensive workloads - Exposure to large-scale data platforms (data lakes, warehouses, streaming systems)