Founding AI Engineer – Build Agentic Systems That Will Transform Hardware Engineering
Techmunity are exclusively hiring a Founding AI Engineer to help build the intelligent core of a new kind of software — one that could transform how robots, rockets, and machines are designed and built.
This is a role for deep builders — those who want to go beyond tweaking prompts and instead design entire agentic systems from scratch: how they plan, reason, and execute in real-world engineering environments.
Software has had its Copilot moment. This AI startup is building a collaborative platform for mechanical, aerospace, and robotics engineers — where AI agents help teams plan, simulate, and run workflows 10x faster.
The goal: unlock the next generation of physical innovation.
The company is led by second-time founders with elite engineering backgrounds:
~ One was lead engineer at Imperial’s Karman Space Programme, shipping code to satellites and leading reusable rocket development
~ The other built London’s first Hyperloop team from scratch, scaled it to 100+ engineers, and went on to launch multiple ventures in deep tech and software
They’re backed by SuperSeed, one of Europe’s leading AI VCs — early investors in breakout companies like Magic.dev, AI Build, and others shaping the future of intelligent infrastructure.
You’ll be one of the first three engineers, joining just before the Seed round — with full ownership of the AI stack and the freedom to shape how intelligent systems are built from day one.
Design abstractions between reasoning, tool execution, and simulation orchestration
Define what “usable AI for engineers” actually looks like
What You’ll Be Building
You’ll work closely with the CTO to architect and implement the intelligence layer of the platform — with real ownership over:
Building orchestration logic for autonomous workflows — handling retries, dependencies, logging, and execution across simulation and design environments
Creating custom interfaces between LLMs and domain-specific tools like CAD/CAE systems (e.g. Contributing to agent UX and task planning tools, working closely with product and frontend engineers
Integrating deeply with a backend built in Python and Rust, deployed on Docker/Kubernetes (AWS)
1–3 years of professional software engineering experience — ideally in fast-moving, early-stage environments
~ Strong Python skills, with a track record of building or deploying LLM-based systems in production
~ Comfort building and testing AI workflows that interact with external APIs, file systems, simulations, and toolchains
~ Bonus: interest or experience in robotics, mechanical/aerospace workflows, or simulation environments
~ A history of high-agency contributions — student teams, startup side projects, open source, or technical competitions
Hybrid: 3–4 days/week in their Hoxton office (London)
Competitive salary (we're talking top 0.A chance to define a new category of applied AI and own a piece of what comes next