Manchester
Fully Remote (Europe & UK) | Early-Stage Startup | Stealth Mode What We're Building We're a well-funded startup developing a proprietary AI model that takes a fundamentally different approach to machine learning. We're not iterating on existing architectures — we're building something new from the ground up. We need a physicist who can turn theoretical insights into production systems. We need people who understand the theory and formulate the underlying mathematics before any code is written. The Role You'll be developing core components of our AI model, engaging in hands-on work at the intersection of physics, mathematics, and ML engineering. This isn't purely theoretical research. You'll own significant pieces of our model architecture from mathematical foundations through implementation and optimisation. Small, high-calibre team with real ownership. As we scale, opportunities to build and lead teams. We're hiring immediately. What You'll Actually Do Derive mathematical formulations of abstract concepts. Design and implement novel learning approaches informed by physics and mathematics. Take concepts from statistical mechanics, information theory, and dynamic systems to construct first principles algorithms. Optimise using advanced mathematical structures, such as tensor networks, and techniques like reinforcement learning - make these approaches work in a production system. Debug why your theoretically sound approach breaks at scale. Fix it. Ship it. Daily reality includes mathematical derivations and performance optimisation. You'll need to be comfortable moving between theory and systems-level engineering within the same afternoon. Required Qualifications Education & Experience: • PhD in Physics: Theoretical/Statistical/Computational/Applied/Mathematical/Quantum/etc., • 3+ years of post-PhD experience applying physics in industry or research settings. Non-academic research experience is also accepted. Core Technical Areas: Physics: • Quantum Mechanics (Must have), • Multi-Dimensional Tensor Networks (Must have), • Theoretical physics and statistical physics, • Dynamic systems (energy landscapes, emergent behaviours), • Modelling and simulation Mathematics: • Advanced linear algebra, optimisation, numerical methods, • Information theory, • Probability, statistics, • Graph theory Highly Valued: • Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning and graph neural networks, computational optimisation at scale, algorithms and data structures., • Quantum Information Theory, • Experience working with stochastic data Who Thrives Here You work fast in loosely defined environments. Competing priorities don't slow you down. You own problems end-to-end. If you need to learn something to solve it, you learn it. You're comfortable with ambiguity and rapid context switching. Startup pace doesn't rattle you. Clear communicator in English. Self-sufficient but collaborates well. This Won't Fit If: • You need complete specs before starting, • You think rigorous means slow, • Current ML paradigms satisfy you, • Proving theorems appeals more than deploying systems What We Offer Direct Impact: Build proprietary AI architecture from scratch Equity: Meaningful stake as an early team member Growth: Lead teams as we scale Resources: Equipment, conference attendance, publication opportunities Autonomy: Real ownership of technical decisions Flexibility: Fully remote within Europe/UK (Central European timezone business hours) Compensation: Competitive salary and equity package (discussed during interviews) Location Fully remote, Europe/UK-based, available during Central European timezone business hours. May transition to hybrid later. Apply Send your CV to We review applications on a rolling basis and respond promptly to strong candidates.
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