Careers at C3
Build the world's largest virtual GPU cluster.
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.
About C3
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.
Autoresearch at scale.
C3 is building the infrastructure for Karpathy-style autoresearch at scale: agents that propose, run and score experiments around the clock, each placed on the right GPU in any cloud. Our Autoresearch service is in beta.
Open roles
Founding Engineer
Humanity's biggest problems will be solved on the system you build.
The role
- Bring every GPU cloud onto C3: provider adapters for Nebius, Crusoe, Hyperstack, GCP, AWS and the next twenty. Provisioning, images and snapshots, quotas, networking and teardown, with each cloud's quirks hidden behind one interface.
- The job path, end to end: from c3 deploy or an agent's MCP call to a running process on a GPU. Workspace upload, environment reproduction, dataset mounts, streamed logs and artifacts back.
- Route every job: pick the supplier from live capacity, price, performance and reliability, and fail over when one falls down.
- The Go agent: lifecycle, mounts, log streaming, recovery and per-second metering on every machine.
- Autoresearch at scale: the research engines (Karpathy-style agent loops, OpenEvolve, AIDE) and the service that runs them on C3.
- The stack: Go agent and CLI; TypeScript control plane on Cloudflare Workers (Hono, D1); Python SDK; Next.js dashboard.
- Shape C3: you'll help define what C3 becomes, with a path to senior or executive leadership.
- Cambridge: majority in person with the founder and C3's first engineer.
Must have
- 4+ years shipping production code to real users; strong open-source work counts.
- Integrations that stay working: you have built and maintained many integrations against messy third-party APIs, and kept them reliable as those APIs changed.
- Backend and cloud fundamentals: APIs, queues, retries and idempotency; Linux, containers and networking; at least one cloud provider's API in depth.
- AI leverage. Demonstrable custom agentic workflows are required. Show us systems you built, in or out of work, and how they multiplied your output.
Success in the first three months
- Supplier coverage from six to twenty-five on a repeatable adapter framework, about two a week, each with an end-to-end test that provisions a real GPU and runs a job.
Nice to have
- Go and TypeScript; GPU and CUDA environments; Docker image pipelines; MCP or agent tooling; a research background in a computational field with substantial software work.
Write to sam@cthree.cloud with your CV and two sentences on the most interesting thing you have ever built and why it mattered. Excited by impossible infrastructure problems and shaping a company from day one? We'd love to hear from you.
Full role page →Founding Engineer, Distributed Systems
Build the machine layer under the world's largest virtual GPU cluster: microVMs, schedulers and the coordination that keeps them honest.
The role
- Isolation at GPU speed: run untrusted agent code on GPUs inside microVMs (Firecracker, Cloud Hypervisor, KVM) with GPU passthrough, and lighter sandboxes where a VM is too heavy.
- Instant starts: snapshot and restore whole machines (memory, filesystem, environment) so a job lands on a warm GPU in seconds, on any cloud.
- A scheduler for a planet: place thousands of short agent jobs across clouds, regions and GPU types by capacity, price, reliability and data location, with queueing, gang scheduling, preemption and fair share.
- Coordination that survives failure: leases, fencing, reconciliation and exactly-once metering across machines and clouds that fail independently.
- Data at the GPU: stage images, environments and datasets ahead of the job, with content-addressed caches and lazy loading.
- The stack today: Go agents on every machine and a TypeScript control plane on Cloudflare Workers. You help decide what comes next.
- Shape C3: one of the first engineers, with a path to technical leadership.
Must have
- Production experience in at least two of: job scheduling or batch systems (Slurm, PBS, LSF, or your own); orchestration (Kubernetes internals and operators, Nomad, Ray); HPC clusters; virtualisation (KVM, Firecracker, Cloud Hypervisor, QEMU). Tell us what broke and how you fixed it.
- Systems depth: Linux internals (namespaces, cgroups, virtualisation), GPU drivers and the CUDA stack, networking and storage.
- 3+ years in systems or infrastructure engineering. Substantial research and open-source engineering count.
- AI leverage. Demonstrable custom agentic workflows are required. Show us systems you built and how they multiplied your output.
Your first three months
- Ship microVM-isolated GPU execution on at least one cloud, with snapshot restore that starts a job in seconds, and make it the default for agent workloads.
Nice to have
- Synchronisation under partial failure (distributed locks and leases, consensus or leader election, idempotency and reconciliation) from real systems; GPU passthrough (VFIO) and GPU sharing (MIG, MPS); checkpoint and restore (CRIU, VM snapshots); RDMA, InfiniBand or NCCL; systems code in Rust or Go; running production workloads on several clouds. Depth in a few is enough.
Also
- Cambridge: mostly in person.
- Research: optional contributions to C3's research and papers.
Write to sam@cthree.cloud with your CV or GitHub and a few sentences on a compute system you built and why it mattered. Excited by impossible infrastructure problems? Get in touch.
Full role page →Head of Research
Coming soonResearch Scientist
Coming soonFounding GTM Lead
Coming soon
Interested, or know someone?
Write to sam@cthree.cloud. Applications are rolling; start as soon as possible. Or pass this page along.