The next breakthroughs in cancer, fusion energy and our understanding of the universe will be found on GPUs running experiments at massive scale. The agents running these experiments need GPUs in bursts no single cloud can supply. C3 turns the world's fragmented GPU capacity into one network they can use: describe a job once, and C3 finds GPUs, reproduces the environment, positions the data, routes the work and meters only running time. C3 is building the infrastructure layer for automated research.
C3 is turning fragmented GPU compute supply into a single global network, creating the compute infrastructure layer for autoresearch at scale. If you want to work on the hardest problems in GPU computing, across hardware, distributed systems and ML research, this is an opportunity to get in early and help shape an extremely technical business. If you have 3+ years of engineering experience shipping code (exceptional open-source work counts), or can demonstrate excellence in physics, maths, computer science or a related technical field, please reach out.
C3 is writing the operating system for the world's GPUs. Its scheduler has to reason about a planet: capacity that appears and vanishes across hundreds of suppliers, prices that move like a market, datasets too large to move quickly, and agents firing thousands of experiments in bursts. It must predict where demand will land before it arrives, and keep every job running when any cloud beneath it fails. Nobody has built this. The first version runs in production today.
Distributed systems: design and build systems that coordinate GPU workloads across clouds, addressing challenges in scheduling, fault tolerance, distributed state and data movement.
Kernel verification: develop methods to verify GPU kernel correctness and the isolation guarantees provided by OS kernels, enabling secure execution of untrusted workloads.
GPU containerisation: design and develop custom container runtimes optimised for GPU workloads, improving startup time, isolation and hardware utilisation.
Autoresearch at scale: optimise C3's architecture across the full GPU stack, from research orchestration and scheduling to runtime execution and hardware utilisation, to support automated research at scale.
Work across C3: contribute across the product as an early member of the engineering team, taking ownership of projects from core infrastructure to developer tools as the company grows.
Cloud integrations: integrate additional GPU providers and maintain support for existing clouds.
Shape C3: you'll help define what C3 becomes, with a path to senior or executive leadership.
Cambridge: work primarily in person alongside the founder and C3's first engineer.
4+ years of software engineering experience: a track record of delivering and maintaining production software. Substantial open-source contributions count towards this experience.
Experience with complex systems: experience designing reliable systems, making architectural trade-offs and diagnosing failures in production.
Drive and ownership: the ambition to make C3 the infrastructure behind a substantial share of the world's GPU computing, and the initiative and persistence to turn that ambition into working systems.
Custom agentic workflows: demonstrable experience building and using custom agentic workflows, with examples of how they improve your engineering work.
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