Machine Learning Engineer (Junior)

SFA (Oxford); City Of London, Packmoor; On-site; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Competitive package

Job summary

SFA (Oxford) is seeking an energetic engineer/scientist to design, build and publish machine learning and applied AI systems used in mining, processing, HR and finance. The role involves working closely with safety, mining, geology and finance teams across multiple countries, including site visits, with a focus on practical impact on real operations.

The candidate will own data pipelines and ML workflows, handle sensor and drilling data, and interact with cross-disciplinary teams to translate

Qualifications

• Degree in physics, geophysics, engineering, applied mathematics, statistics, machine learning, data science or a closely related discipline
• Strong statistical grounding with model validation and uncertainty quantification
• Ability to reason about physical systems and challenging environments quickly
• Strong knowledge of ML and data science practices
• Familiarity with Python, Git and SQL
• Excellent presentation and written communication skills
• Willingness to travel internationally, including site visits
• Comfortable working across time zones with early mornings and late evenings
• Right to work in the UK (sponsorship position)
• Experience with time series, classification, survival analysis, or Bayesian methods
• Experience with sensor/IoT/drilling or industrial data
• Practical experience with LLM APIs or agent frameworks
• Rock mechanics/geomechanics/geoscience coursework
• Azure or another cloud platform
• Competitive package offered for the right candidate

Responsibilities

• Spend time with specialist teams and learn about supported operations
• Build and maintain data pipelines and production ML systems
• Analyse sensor, telemetry and operational data with statistical rigour
• Map real workflows and design agentic AI tools to automate them
• Present findings to technical and operational audiences, including senior stakeholders

Skills

Python

Git

SQL

Machine learning

Data science

Statistics

Time series

Classification

Survival analysis

Bayesian methods

LLM APIs

Agent frameworks

Rock mechanics

Geomechanics

Geoscience

Azure cloud

Education

Relevant degree (Physics / Geophysics / Engineering / Mathematics)

Tools

Azure cloud

Job description

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.

This 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 do

• Spend time working with specialist teams, learning about the operations you support
• Build and maintain data pipelines and production machine learning systems
• Analyse sensor, telemetry and operational data with appropriate statistical rigour, including quantifying uncertainty
• Map real workflows with operational teams and design agentic AI tools to automate them
• Present findings to technical and operational audiences, including senior stakeholders

Requirements for the role

• Degree in physics, geophysics, engineering, applied mathematics, statistics, machine learning, data science or a closely related discipline
• Strong statistical grounding, including model validation and uncertainty quantification
• The ability to reason about physical systems and understand conceptually challenging environments quickly
• Strong knowledge of Machine Learning and Data Science practices
• Strong Python, with working knowledge of Git and SQL
• Excellent presentation and written communication skills
• Willingness to travel internationally, including site visits, subject to site induction and occupational health requirements
• Comfortable working across time zones with some early mornings and late evenings possible
• Right to work in the UK [confirm sponsorship position]
• Time series, classification, point processes, survival analysis or Bayesian methods
• Sensor, IoT, drilling or other industrial data
• Practical experience with LLM APIs or agent frameworks
• Rock mechanics, geomechanics or geoscience coursework
• Azure or another cloud platform
• Competitive package offered for the right candidate