Staff Backend Engineer

Join Stream as a Staff AI Engineer, where you'll take ownership of model development on our AI team. You'll be responsible for building, fine-tuning, evaluating, and deploying models that directly impact systems serving over a billion users. You'll work across the engineering organization, interface with API teams and infrastructure, and contribute to the open-source ecosystem. Enjoy a range of benefits, including generous time off, stock options, comprehensive health coverage, and more.

• Conduire le développement, le réglage fin et l'évaluation des modèles d'IA internes, de la conception des ensembles de données jusqu'au déploiement en production.
• Construire et maintenir les pipelines de données qui alimentent l'entraînement des modèles, en veillant à la qualité des données, à l'étiquetage et à la reproductibilité.
• Prendre les modèles en production sur la pile de services de Stream, en les ajustant pour la latence, le coût et la fiabilité à volume élevé.
• Hands-on machine learning experience, specifically with supervised fine-tuning and post-training of models
• Demonstrated ownership: a track record of picking up ambiguous problems and driving them to a result without waiting for direction
• Cloud experience with at least one major provider (GCP or AWS), including infrastructure-as-code with Terraform
• Strong communication skills and comfort working in a small, distributed, fast-moving team
• Familiarity with the modern fine-tuning and serving toolchain, e.g. Unsloth, Fireworks, Baseten, or equivalents
• 5+ years of production-level Python engineering experience, with code you have shipped and maintained rather than only prototyped
• Experience designing and operating data pipelines for training and evaluation; a data engineering background is a strong route into this role
• Experience running ML-based products in production: not just training models, but owning them through deployment, monitoring, retraining, and iteration against real usage
• A visible open-source footprint: libraries you have authored or maintained, meaningful GitHub activity, or contributions to AI model repositories
• Experience with Go (all of Stream's APIs use Go, so it helps when interacting with other teams)
• Experience with real-time or low-latency inference systems
• Deep understanding of Python's concurrency model and asyncio's limitations in high-throughput systems
• Experience as an early engineer or founder, or otherwise operating at startup pace with an undefined roadmap