Staff Backend Engineer – AI

About Stream

Stream ( GetStream.io )'s SDKs and APIs enable devs to build activity feeds, chat, and voice/video, powering over a billion end users worldwide . Trusted by top brands like Strava, Nextdoor, Patreon, and eBay, we're on a mission to make real-time communication and social experiences seamless for developers and their users.

We're also the makers of Vision Agents , the open-source Python framework for building low-latency voice and video AI agents: over 8k stars on GitHub, 35+ model integrations, and sub-500ms latency on our global edge network.

Role Overview

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.

Location: Office in Amsterdam or Remote (Europe). Occasional travel for in-person collaboration and meetups is encouraged.

Hybrid policy: applicants based in the Netherlands or relocating here, are expected to work in the office in Amsterdam 3 times per week. Exemptions apply to specific cases.

What will you work on

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.

About You

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.