About the role
This is an Atlantic portfolio venture.
About the Venture
We are building a new path to fusion energy. Every major leap in human civilisation has followed a breakthrough in energy, from fire to steam to electricity, and we believe fusion is the next one. Our bet is that it will not come from building bigger machines. It will come from controlling matter with enough precision that fusion becomes a manufacturable technology, atoms aligned exactly enough that fusion happens under controlled conditions, on a chip. This is a semiconductor-scale path to clean power, and our stance is precision over brute force. We start in physics and simulation, then build the physical proof.
About the Role
Reaching a chip-scale approach means working through physics and simulation at a depth that is hard to reach by conventional means, and to do that we need a new class of scientific intelligence. You will work directly with the founders to design that system from the ground up: an AI-driven research platform that can read papers, connect ideas across disciplines, orchestrate simulations, evaluate hypotheses and learn from results. This is not a chatbot or prompt engineering role, and we are not building a foundation model or competing with OpenAI, Anthropic or Google. We believe these systems will need richer representations than today's token-prediction models, so you will help explore new architectures for scientific reasoning, memory, hypothesis generation and autonomous discovery. The better that system works, the faster we get to the physical proof.
What you'll do
Design multi-agent research systems and the orchestration that ties them together
Build long-term memory architectures for scientific reasoning
Create paper ingestion and knowledge extraction pipelines
Develop scientific reasoning workflows and connect AI agents to simulation environments
Build autonomous experiment and evaluation loops
Design retrieval, planning and orchestration systems
Integrate state-of-the-art LLMs and open-source models
Develop scalable infrastructure for continuous learning
Explore next-generation AI architectures for scientific discovery
About You
We care far more about what you have built than about formal credentials. You will thrive here if you enjoy solving problems nobody has solved before, learn quickly and independently, and are comfortable with uncertainty. This role suits someone who wants to help create a new category of scientific intelligence rather than optimise an existing product.
You have already built real agent systems, with strong experience across several of: multi-agent architectures, tool use and function calling, agent orchestration, planning systems, long-term memory, knowledge graphs, autonomous research workflows, RAG architectures and evaluation frameworks
You write production-quality software with strong Python skills, and you are comfortable with API design and integration
You know your way around cloud infrastructure, Docker and containerisation, and databases including vector databases
You are comfortable with the mathematical ideas this work draws on, such as linear algebra, optimisation, probability, graph theory and dynamical systems
You do not need to be a theoretical physicist, but you should enjoy working on highly technical scientific problems
Nice to have:
Physics simulations, scientific computing or computational physics
HPC environments
Reinforcement learning
AI for Science
Quantum computing
Scientific publishing workflows
Open-source AI frameworks
Why Join
Our goal is bigger than software. The systems you build will be used to accelerate research in fusion energy, scientific discovery, advanced simulation, AI for physics and quantum technologies. Success here is measured in real scientific progress.
Work with an exceptional founding team with scientific and commercial track records across places like LMU, Apple, Amazon, Eurazeo, Sprin-D and McKinsey. Around them is a small group of physicists, simulation experts and AI specialists who value curiosity, independent thinking and the courage to challenge assumptions.
We are remote-first, so you can work from Munich, Berlin, London, Lisbon or wherever you do your best work. We keep bureaucracy to a minimum so talented people can move fast and follow promising ideas.
This is a chance to help build something before it becomes obvious, and to help solve one of humanity's hardest problems.
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