Founding Research Engineer
You will design and build AI reasoning systems for physical chip design, using post-training pipelines (fine-tuning, RL, reward modelling) and synthetic data generation for a low-data domain. As the first AI hire at a seed-stage deep-tech startup, you will work directly with co-founders and a chip architect on model architecture, training dynamics, and inference optimisation.
Free Tailor for ATS: 10/10 runs left
Founding Research Engineer
Deep Tech Startup | London
Compensation: up to £120k base + founding equity package
About
Building a chip takes hundreds of engineers and years of work. Physical design — the placement, routing, and timing of transistors — is one of the hardest stages: thousands of interdependent decisions, sparse domain data, and no general-purpose AI that understands circuit structure. This role exists to change that.
This is an early-stage startup backed by top EU investors, building an AI reasoning system for physical chip design. You'd be the first AI hire, working directly alongside the co-founding team and a founding chip design architect.
What you'll do
Design and build AI reasoning systems that understand circuit structure and make design decisions autonomously
Build post-training pipelines — fine-tuning, reward modelling, and feedback-driven learning on proprietary chip design data
Develop synthetic data generation strategies for a domain with limited open-source examples
Drive data strategy, model architecture, and inference optimisation across the full AI system
Work directly with the founding chip design architect to understand problem structure and translate it into training signals
Shape technical direction alongside the founders — framing problems, proposing approaches, designing experiments
What you'll need
Deep, hands-on experience building AI systems for hard, novel problem domains — reasoning, mathematics, biology, physics, or similar
Ability to build reasoning models from scratch: post-training, RL, fine-tuning, data strategy — not just deploying existing pipelines
Strong fundamentals across model architecture, training dynamics, and inference optimisation
First-principles mindset: given a new structured problem, you can understand it and build an AI system to solve it
Startup mindset: comfortable with ambiguity, able to move fast without a large team behind you
Optional Bonus
Experience with symbolic reasoning, graph-structured data, or formal methods
Background in post-training for reasoning or maths-style models
Synthetic data generation for low-data domains
Shortlisted candidates will be contacted within 48 hours.