Machine Learning Engineer (Junior)

Position based in London and/or our Oxford office, this opportunity is to join the growing Analytics, Machine Learning and Robotics team at SFA, a specialist critical minerals intelligence firm.Job descriptionThis position suits an early-career and/or experienced engineer or scientist with a background in physics, engineering, mathematics, statistics, machine learning, or data science who wants to own, design, build and publish machine learning and applied AI engineering systems used in real mining, processing, and other business operations such as HR and finance.This is a forward deployed role. You will be employed by SFA and work exclusively on mission critical projects related to engineering, mining and refining, which will see you interact with safety, mining, processing, geology and finance teams across multiple countries. The work means going to where the problems are, understanding the physical and operational process behind the data, and building systems that people on site actually use.Projects span operational forecasting, operational optimisation, sensor and drilling data, rock mechanics and seismicity, and agentic AI systems that automate real workflows. Much of what we model is physical, so physical intuition and statistical rigour matter as much as coding ability. This role is as human-facing as anything else, requiring you to learn from experienced stakeholders in complex, demanding environments and then generalise issues from mines to day-to-day processing operations.Although this is a junior position, you will work closely with an experienced engineers from day one and take on workstreams and stakeholder relationships as you progress. These projects will start with understanding and working with an operational optimisation issue at the heart of our clients’ activities. International travel is required, including site visits and having face-to-face engagements with specialist teams worldwide. This role would suit a curious individual who enjoys working and understanding complex data problems with real-world issues where the data for sensors might not be there, or where there is a lack of knowledge data which is often messy or damaged; you must find a way to understand and then present to people who do not have time to understand what or how you did it but want to know the outcomes.What you will doSpend time working with specialist teams, learning about the operations you supportBuild and maintain data pipelines and production machine learning systemsAnalyse sensor, telemetry and operational data with appropriate statistical rigour, including quantifying uncertaintyMap real workflows with operational teams and design agentic AI tools to automate themPresent findings to technical and operational audiences, including senior stakeholdersRequirements for the roleDegree in physics, geophysics, engineering, applied mathematics, statistics, machine learning, data science or a closely related disciplineStrong statistical grounding, including model validation and uncertainty quantificationThe ability to reason about physical systems and understand conceptually challenging environments quicklyStrong knowledge of Machine Learning and Data Science practicesStrong Python, with working knowledge of Git and SQLExcellent presentation and written communication skillsWillingness to travel internationally, including site visits, subject to site induction and occupational health requirementsComfortable working across time zones with some early mornings and late evenings possibleRight to work in the UK [confirm sponsorship position]DesirableTime series, classification, point processes, survival analysis or Bayesian methodsSensor, IoT, drilling or other industrial dataPractical experience with LLM APIs or agent frameworksRock mechanics, geomechanics or geoscience courseworkAzure or another cloud platformCompetitive package offered for the right candidateHow to applySend a CV and a short covering note to info@sfa-oxford.com. Tell us about one thing you built and one thing you got wrong.Interview processThe 1st Stage will be an introductory call with the hiring managers, including a discussion about yourself, an introduction to us, and a few technical questions.2nd Stage: will be a take-home assessment on a case study from within similar current operations we are facing3rd Stage: a presentation to the hiring manager and a few members of the group (online)4th Stage: an in-person interview with the wider group and lunch with the hiring team5th and final stage: Will be an offer and onboarding