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# Machine Learning Engineer

- **Company:** Orbital Industries Limited
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Software Quality, Software Debugging, Machine Learning, Open Source Technology, Software Engineering, Systems Architecture, Large Language Models, Deployment Automation, Machine Learning Operations, Software Coding
- **Published:** September 2, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=27711246a95f0056

## About the Role

* Significant software engineering and ML experience, with depth in training, evaluating and deploying AI models - demonstrated through industry work
 * Proven experience training, evaluating and productionising AI models at scale, with deep understanding of the full ML lifecycle from research to deployment
 * Strong engineering fundamentals with the ability to write high-quality, maintainable code and architect robust systems
 * A strong ability to reason about algorithms, system design, linear algebra, probabilistic concepts and ML engineering trade-offs
 * An ability to debug complex machine learning systems through meticulous attention to detail, testing of edge cases and carefully selected ablations
 * A genuine interest in building AI systems that enable breakthrough scientific and industrial applications
 * Upon reading Hamming's You and Your Research, you resonate with quotes such as:
 + "Yes, I would like to do first-class work"
 + "You should do your job in such a fashion that others can build on top of it, so they will indeed say, 'Yes, I've stood on so and so's shoulders and I saw further.'"
 + "Instead of attacking isolated problems, I made the resolution that I would never again solve an isolated problem except as characteristic of a class"

Bonus: Experience with physics-informed or chemistry-focused AI applications. Experience building or fine-tuning large language models. Experience with agent-based systems, tool use or agentic workflows. Contributions to open-source ML projects or published research.

## Description

Set the technical bar and ensure engineering excellence

 * Establish and maintain exceptionally high standards for code quality, system architecture and ML research and engineering practices through hands-on coding and technical review
 * Design robust, well-engineered systems that others can build upon, balancing research velocity with production requirements
 * Drive technical decisions on model selection, training approaches and deployment strategies

Deliver high-impact AI projects across diverse domains

 * Develop and deploy AI solutions across the entire technology development pipeline- computational chemistry simulations, agentic workflows and beyond
 * Rapidly upskill in new technical areas through close collaboration with domain experts (no prior chemistry or materials experience required)
 * Demonstrate strong implementation skills through hands-on development, contributing significantly to the codebase
 * Balance research rigour with pragmatic engineering to deliver production-ready systems at scale

Push the frontier of ML research

 * Design and implement novel ML architectures for complex scientific domains, with work that meets publication standards at top-tier conferences
 * Drive research projects from conception through to deployment, showing initiative and technical depth
 * Engage continuously with the latest ML literature, staying current with developments in foundation models, generative AI and scientific machine learning