Computational Physicist - ML engineer in UNITED KINGDOM

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. Not iterating on existing architectures - building something new from the ground up. 

We need a physicist who can turn theoretical insights into production systems. People who understand why the math works before they write the code. 

The Role 

You'll be developing core components of our AI model - hands-on work at the intersection of physics, mathematics, and ML engineering. This isn't research theatre. You'll own significant pieces of our model architecture from mathematical foundations through implementation and optimisation. 

Small team. High calibre. Real ownership.

As we scale, opportunities to build and lead teams. We're hiring immediately. 

What You'll Actually Do 

Design and implement novel learning approaches informed by physics and mathematics. Take concepts from statistical mechanics, information theory, and dynamic systems - make them work in a production ML system. 

Debug why your theoretically sound approach breaks at scale. Fix it. Ship it. 

Daily reality: mathematical derivations, 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 or Mathematics (strong mathematical physics background preferred) 
• 5+ years professional ML/AI engineering, data science, or equivalent 

Core Technical Areas: 

Mathematics: 

• Advanced linear algebra, optimisation, numerical methods 
• Probability, statistics, information theory 
• Graph theory 

Physics: 

• Statistical physics and theoretical physics 
• Dynamic systems (energy landscapes, emergent systems) 
• Modeling and simulation 

Machine Learning: 

• Parameter-free learning approaches 
• Bayesian methods and belief systems 
• Unsupervised learning and Graph Neural Networks 
• Computational optimisation at scale 
• Strong foundation in algorithms and data structures 

Engineering: 

• Python proficiency 
• ML frameworks (PyTorch, TensorFlow, or equivalent) 
• Distributed training systems 
• Translating theory into working implementations 

Nice to Have 

Knowledge graphs, reinforcement learning, and ontology development 

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. 

Currently fully remote, but flexible if we need hybrid arrangements later. 

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: Hardware, equipment, conference attendance, publication opportunities 

Autonomy: Real ownership of technical decisions 

Flexibility: Fully remote within Europe/UK (CET hours) 

Compensation: Competitive salary and equity package (discussed during interviews) 

Location 

Fully remote, Europe/UK based, available during CET business hours. May transition to hybrid later. 

Apply 

Send your CV to recruitment@huberta.io 

We're reviewing applications on a rolling basis and responding quickly to strong candidates.