Applied Scientist, Machine Learning

Applied Scientist 

Machine Learning · Physical Modelling Team 

The Company  

OctaiPipe is a young, ambitious company with the vision to be the global driving force for the next paradigm of foundational, physical AI that ensures our connected world, and its critical infrastructure, is safe, secure and sustainable. We are growing fast, having closed a recent funding round and looking to accelerate rapidly. OctaiPipe is offering the right candidate an exciting role on this adventure!   

OctaiPipe is on a mission to revolutionise the optimisation of energy in data centres through decentralised artificial intelligence (AI). To do this, OctaiPipe is harnessing an elegant but revolutionary idea. Rather than move the data from the source, move the algorithms to the data to learn at the data source. This learning can be achieved with the intelligence of many devices through novel federated AI technology. OctaiPipe is developing the AI for Cooling Efficiency (ACE) application to be deployed using its own in-house distributed AI platform. 

The Role

We are looking for an Applied Scientist, Machine Learning to join our Physical Modelling team. You'll develop data-driven surrogate and digital twin models for real industrial assets, exploring how they're built, evaluated and continuously improved using data from live deployments. You'll frame research questions, design and run experiments, and translate insights into robust models that our engineering teams can take into production. A key part of the role is understanding how these models behave in high-stakes environments and helping determine when they can be trusted to support real-world decision-making.

Duties and responsibilities 

• Develop, evaluate and advance the state of the art in data-driven surrogate / digital-twin methodologies. 

• Own a research agenda end to end - from problem framing through experimentation to results that production systems consume - with a high degree of independence. 

• Help develop and validate methods for continual learning
  of deployed models as new operational data becomes available. 

• Contribute to rigorous evaluation standards for machine-learning models used in high-stakes, real-world settings. 

• Collaborate closely with applied scientists, engineers and neighbouring teams, and communicate findings clearly to technical and non-technical audiences. 

Your profile 

• A strong research background in machine learning - demonstrated by a PhD, a publication record, or equivalent applied research experience in industry. 

• Depth in at least two of: continual or transfer learning; uncertainty quantification and Bayesian machine learning; time-series or dynamical-systems modelling; active learning, safe exploration or Bayesian optimisation. 

• Hands-on experience training, fine-tuning and evaluating deep-learning models (PyTorch or similar). 

• A track record of owning a research thread end to end and delivering results that others could build on. 

• A rigorous empirical mindset: you design evaluations before you trust results, and you are honest about failure modes. 

• Clear written and verbal communication. 

You also might have 

• Experience applying machine learning to physical systems - energy, HVAC, buildings, industrial processes, or robotics. 

• Familiarity with reinforcement learning or model-based control. 

• Experience with limited-data regimes or with training on simulated data. 

• Experience shipping research into production alongside engineers. 

• Publications or open-source contributions in relevant areas. 

Why Join OctaiPipe  

• Work on real-world sustainability impact at global scale  

• Influence how AI is responsibly applied to critical infrastructure  

• Join a well-funded, rapidly growing scale-up with ambitious goals  

• Collaborate with experts across AI, infrastructure, and operations  

• Shape a product that can materially reduce energy use and carbon emissions worldwide  

The above statements are not intended to encompass all functions and qualifications of the position; rather, they are intended to provide a general framework of the requirements of the position. Job incumbents may be required to perform other functions not specifically addressed in this description.