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Mind Foundry

Explainable AI platform for high-stakes decisions

Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeOxford, UK

About the Job

Requirements

  • A degree in Computer Science, Applied Mathematics, Statistics, Physics, or a related STEM field, or equivalent practical experience.
  • Strong programming skills in Python, including producing clean, reproducible, well-tested, and well-documented code.
  • Hands-on experience with production infrastructure, which may include Docker, Linux, CI/CD, MLOps, cloud platforms, and model serving architectures.
  • Familiarity with modern machine learning libraries and tooling, and the ability to select pragmatic approaches for real-world problems.
  • Ability to work in ambiguous, fast-moving environments, including alongside delivery teams, partners, or customers where required.
  • Ability to communicate technical ideas, model behaviour, and trade-offs clearly to technical and non-technical audiences.
  • Must hold existing UK Security Vetting (SC) or be eligible for it.

Responsibilities

  • Design, build, and iterate machine learning prototypes and production-ready capability for complex, real-world Defence and National Security use cases.
  • Translate research outputs, experimental approaches, and customer problems into maintainable, testable, and deployable machine learning systems.
  • Work with real-world data sources, including noisy, incomplete, high-volume, or streaming data, to develop robust and operationally useful approaches.
  • Collaborate with Machine Learning Scientists, Software Engineers, Product Managers, and customer-facing teams to shape technically credible and user-relevant solutions.
  • Write clean, maintainable, and well-tested code, contributing to engineering standards, reproducibility, and long-term ownership.
  • Support evaluation, validation, and assurance of machine learning models, including communicating model behaviour, limitations, and trade-offs to technical and non-technical stakeholders.
  • Contribute to deployment approaches for constrained or complex environments, including edge settings where compute, power, and bandwidth may be limited.
  • Provide technical input into bids, demonstrations, customer discussions, and partner engagements where required.
  • Keep up to date with emerging machine learning methods, tools, and implementation approaches, and share relevant knowledge with the wider team.
  • Travel to and work from client sites and partner locations; when not onsite, work office-based or hybrid from the Summertown, Oxford office.

Desired Qualifications

  • Prior experience working with government customers, defence contractors, or in military environments.
  • Ability to produce clear technical material, such as documentation, internal technical notes, reports, or publications.
  • Experience in model development, data processing, and streaming using tools such as Spark and Kafka; Python microservices using Flask or FastAPI; and interactive visualisations and user interfaces using Streamlit, Plotly, or Gradio.
  • Experience or knowledge of signal processing, data fusion, model evaluation, or AI/ML assurance.

About the Company

Mind Foundry

Explainable AI platform for high-stakes decisions

Mind Foundry builds enterprise AI software focused on high-stakes decisions in sectors like insurance, infrastructure, and defense. It offers the Mind Foundry Platform, a toolset that lets data scientists and business users design, test, deploy, and monitor responsible, explainable AI models. The platform supports human-AI collaboration, model governance, and ongoing monitoring, applying Bayesian inference and probabilistic methods to real-world problems such as fraud detection, pricing optimization, claims management, and infrastructure health monitoring. The company differentiates itself through its research-backed approach from the University of Oxford, emphasis on explainability and governance for trusted AI, and strategic partnerships (e.g., ANDI) that target large enterprises and public sector clients. Its goal is to help organizations deploy transparent, reliable AI that improves decision-making while maintaining control over model behavior and risk.

Company size
51-200
Company stage
Grant
Total funding
$59.9M
Headquarters
Oxford, United Kingdom
Founded
2016

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