Founding Member of Technical Staff

Cambridge Compute Co.; Cambridge; Remote; GBP 70,000 - 110,000; Full time

Job summary

Cambridge Compute Co. is building a global GPU compute network that automates experiments, data placement and cost-aware scheduling. You will work on the hardest problems in GPU computing, across hardware, distributed systems and ML research, contributing to the core infrastructure today.

The role requires a STEM background and proven ability to ship high-quality code, with open-source contributions valued. You will join a technically ambitious team in Cambridge.

Qualifications

• 3+ years of engineering experience shipping code (exceptional open-source work counts).
• Excellence in physics, maths, computer science or a related technical field.
• Standout open-source, business, industry or startup achievements.
• Experience building agentic workflows and multi-agent systems.
• Experimental judgement and ownership: learn quickly and carry work to a useful result.

Responsibilities

• Develop and evaluate methods for predicting demand, placing workloads and improving GPU utilisation.
• Work on frontier autoresearch: tackle open problems using state-of-the-art methods for agents that design, run and assess experiments.
• Profile, benchmark and optimise C3’s codebase for speed, efficiency and GPU utilisation.
• Collaborate with Research leadership to apply promising results to production.
• Contribute to reproducible research with reliable code, benchmarks and analyses.

Skills

Engineering experience shipping code

Open-source contributions

Physics/Math/CS excellence

Education

STEM degree or equivalent

Job description

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.

Models and experiments: develop and evaluate methods for predicting demand, placing workloads and improving GPU utilisation.

Frontier autoresearch: tackle open problems using state-of-the-art methods for agents that design, run and assess experiments.

Performance: profile, benchmark and optimise C3’s codebase for speed, efficiency and GPU utilisation.

Research into production: work with the Head of Research and engineering team to apply promising results to C3.

Reproducible research: build reliable research code, benchmarks and analyses, contributing to publications where appropriate.

Exceptional technical ability: a STEM degree at any level, or equivalent knowledge, plus standout open-source, business, industry or startup achievements.

Agentic expertise: demonstrable experience building custom agentic workflows and multi agent systems.

Experimental judgement: formulate problems, test ideas rigorously and interpret results.

Ownership: learn unfamiliar subjects quickly and carry work through to a useful result.