Machine Learning Systems Engineer

About Attention Engineering

The gap between how top engineers around the world use AI systems today - elaborate coding agent configs, massive context files, and engineered workflows - and how everyone else uses it is only growing.

We think the company that bridges the gap to create a sovereign product that understands and multiplies the rest of the worlds output will be one of the most important companies built in this decade.

The Team

We’re a small team of builders/researchers. We’ve worked at companies like Citadel, Jane Street, and Optiver, published papers at the top AI conferences, won IOI gold medals, and more.

If your background doesn’t fit cleanly into one of the buckets above, that’s fine. We care about ownership, experience building, and slope, rather than your pedigree.

The Role

We are hiring one title across multiple strengths. You will own large, critical parts of the system. We hire across a few different spikes, but the bar is exceptional individuals. You’re spike may be:

• AI Runtime & Agent Orchestration: context management, tool use, VLMs, model selection, output quality in production.

• Search, Retrieval, Vector Systems & Data Infrastructure: Embedding pipelines, ranking and retrieval architecture, and data infrastructure underneath.

• Evals, Applied ML & Model Quality.

• Backend Systems & Workflow Architecture: PostgreSQL, Redis, Pub/Sub, FastAPI

• Infrastructure, Cloud Platform & Reliability: GCP, Terraform, Docker, Cloud Run.

You will work directly on the product, platform, and infrastructure with no layers between you and the decisions that matter.