Physicist

Fully Remote (Europe & UK) | Early-Stage Startup | Stealth ModeWhat We're BuildingWe'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 RoleYou'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 DoDerive 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 QualificationsEducation & 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 physicsDynamic systems (energy landscapes, emergent behaviours)Modelling and simulationMathematics:Advanced linear algebra, optimisation, numerical methodsInformation theoryProbability, statisticsGraph theoryHighly 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 TheoryExperience working with stochastic dataWho Thrives HereYou 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 startingYou think rigorous means slowCurrent ML paradigms satisfy youProving theorems appeals more than deploying systemsWhat We OfferDirect Impact: Build proprietary AI architecture from scratchEquity: Meaningful stake as an early team memberGrowth: Lead teams as we scaleResources: Equipment, conference attendance, publication opportunitiesAutonomy: Real ownership of technical decisionsFlexibility: Fully remote within Europe/UK (Central European timezone business hours)Compensation: Competitive salary and equity package (discussed during interviews)LocationFully remote, Europe/UK-based, available during Central European timezone business hours. May transition to hybrid later.ApplySend your CV to review applications on a rolling basis and respond promptly to strong candidates.

Created: 2025-11-18