Senior Machine Learning Engineer

Join Unity's ML Engineering Infrastructure team as a Senior Machine Learning Engineer. In this role, you will build and maintain data and feature pipelines, develop infrastructure for distributed training workflows, and contribute to model serving and deployment infrastructure. You will work across the full ML infrastructure stack and take on well-scoped projects, delivering them end-to-end using your own judgment. This position offers comprehensive benefits, including health insurance, employee stock ownership, generous vacation, and personal days.

- Construire et maintenir des pipelines de données et de fonctionnalités qui génèrent des ensembles de données d'entraînement pour les modèles d'apprentissage automatique et l'expérimentation. - Développer et améliorer l'infrastructure pour les flux de travail d'entraînement distribué, et construire et exploiter des pipelines ML multi-étapes en utilisant des outils d'orchestration de flux de travail. - Contribuer à l'infrastructure de déploiement et de service des modèles, en aidant les modèles à passer de manière fiable de l'entraînement à la production.

- Experience with data pipelines, model training workflows, or large datasets beyond academic settings - Ability to spot patterns in ambiguous or inconsistent problems and translate them into practical technical needs - Comfortable working across several layers of the ML stack, with a track record of learning new tools and systems quickly - Strong Python skills and experience working with data-intensive workloads - Clear communication and a collaborative approach with peers, managers, and partner teams - Hands-on experience with ML frameworks (e.g., PyTorch) and distributed compute (e.g., Ray, Spark) - Bachelor's degree in Computer Science, Machine Learning, Systems, or a related field, or equivalent practical experience - Experience building ML infrastructure, data platforms, or distributed systems in production - Experience with Kubernetes and cloud infrastructure (GCP or AWS) - This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English - Experience with observability tooling (e.g., Prometheus, Grafana) - Experience with model serving frameworks (e.g., Triton, KServe, Ray Serve) - Experience with workflow orchestration systems (Airflow, Prefect, etc.) - Familiarity with feature stores or experimentation platforms - Exposure to large-scale data platforms (data lakes, warehouses, streaming systems such as Kafka)