Multi-agent systems engineer

Who we are
FractalBrain is building the intelligence layer for a world of continually adapting machines. Our thesis is that human-inspired robotics will drive near-term adoption, while embodied multi-agent systems and adaptive, custom-fit robots will increasingly dominate industry and research needs. The enduring opportunity is intelligence that grows across bodies and coordinated teams, accumulating skills through experience.

What we're building
Today's AI is trained once and then frozen. Everything it knows is fixed before it meets the world, and teaching it something new risks breaking what it already knows.

Brains don't work that way. They keep learning for a lifetime, grow new connections when they need them, and look at the world selectively instead of processing every pixel at full resolution.

What you'll work on. You'll build environments and benchmarks for continual multi-agent learning. These are open-ended worlds where goals shift and other agents adapt over time. You'll run experiments in them, find where our agents fail, and work with the core team to fix it. Some of this is research. A lot of it is systems engineering, because our models are large sparse graphs rather than tensors, and making them fast is an open problem.

Who you are. You've worked on reinforcement learning, multi-agent systems or continual learning, and you've shipped code other people relied on. You read papers critically and can tell when a benchmark measures less than it claims. You enjoy open-ended problems and fast iteration, and you don't need an existing framework to get started.