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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Scientist, Machine Learning - **Company:** Octaipipe - **Location:** UK - **Contract:** Permanent contract - **Skills:** Machine Learning, Operational Data Store, Reinforcement Learning, Pytorch, Transfer Learning, Deep Learning, Data Analytics - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/applied-scientist-machine-learning-octaipipe-8733824 ## About the Role * 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. ## Description 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. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Model to Metal: An Open Source Stack for Accelerating Intelligence](https://www.wearedevelopers.com/videos/1636-from-model-to-metal-an-open-source-stack-for-accelerating-intelligence) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)