Staff Backend Engineer, Stream

• 5+ years of production-level Python engineering experience, with code you have shipped and maintained rather than only prototyped
• Hands-on machine learning experience, specifically with supervised fine-tuning and post-training of models
• Familiarity with the modern fine-tuning and serving toolchain, e.g. Unsloth, Fireworks, Baseten, or equivalents
• Cloud experience with at least one major provider (GCP or AWS), including infrastructure-as-code with Terraform
• Experience running ML-based products in production: not just training models, but owning them through deployment, monitoring, retraining, and iteration against real usage
• Experience designing and operating data pipelines for training and evaluation; a data engineering background is a strong route into this role
• Demonstrated ownership: a track record of picking up ambiguous problems and driving them to a result without waiting for direction
• Strong communication skills and comfort working in a small, distributed, fast-moving team

Desirable

• We're seeking a Staff AI Engineer to own model development on our AI team. You'll build, fine-tune, evaluate, and ship the models that run inside Stream's products, end-to-end, from dataset design through to production
• As the technical owner, you will drive key decisions independently, shipping models that directly impact systems serving over a billion users
• Own the development, fine-tuning, and evaluation of in-house AI models from dataset design through to production deployment
• Run supervised fine-tuning and post-training experiments, establishing the benchmarks and evaluation harnesses that tell us whether a model is actually good enough to ship
• Build and maintain the data pipelines that feed model training, keeping data quality, labelling, and reproducibility to a high standard
• Take models to production on Stream's serving stack, tuning for latency, cost, and reliability at high volume
• Set the technical direction for an intentionally undefined problem space, deciding what to build, what to test, and what to abandon
• Work across the wider engineering organisation, interfacing with Go-based API teams and infrastructure to get models integrated into the product
• Contribute to the open-source ecosystem where relevant, and share work publicly through code, writing, or community engagement
• Raise the bar on engineering standards across the AI team through code review, mentorship, and pragmatic best practices