Senior Machine Learning Engineer; Platform

Location

On site.

In a world of immense uncertainty, digi

Lab is a pioneering AI company that empowers governments and organisations in safety‑critical or highly regulated industries to solve critical, complex, and high‑stakes challenges using machine learning and uncertainty quantification. From forging a path to clean energy to life‑saving medical diagnostics and beyond, making critical decisions with unwavering confidence is difficult, especially when data is complex, sparse, or incomplete. This is where digi

Lab's expertise shines through. Our trustworthy and explainable AI platform, The Uncertainty Engine, supported by our team of machine learning specialists and data scientists, enables decision‑makers to accelerate innovation, reduce the risk of failure, turn insight into action, and deliver greater value through more informed and confident decisions.

The Senior Machine Learning Engineer (Platform) is a full‑time position (Monday to Thursday), reporting directly to the Lead Software Engineer. This role is central to the ongoing development and maintenance of digi

Lab's core product, The Uncertainty Engine. It sits at the intersection of probabilistic machine learning, uncertainty quantification, and large‑scale scientific software, with a strong focus on Python development within a fast‑paced, collaborative, and dynamic engineering environment.

• Collaborating with a cross‑functional team of engineers, scientists to help lead on the design, development, and maintenance of high‑quality software solutions
• Collaborate with product management to translate business requirements into technical solutions
• Contribute to architectural design, development, testing, and deployment of productionised probabilistic machine learning models and uncertainty quantification techniques
• Build abstractions and APIs for probabilistic modelling, inference, and uncertainty propagation within The Uncertainty Engine
• Support experimentation with amortised inference and surrogate models for expensive simulators
• Optimise and scale Monte Carlo–based methods
• Utilise expertise in AWS, Python, Mongo
  
  DB, and other relevant technologies to build scalable, systems
• Foster a collaborative, learning‑oriented environment within the team
• Champion “Scrum” and contribute to team process improvements
• Provide technical support and lead incident investigations

Duties may evolve, and you may be asked to take on other reasonable responsibilities within your competence to support our growth.

• Demonstrable experience of developing machine learning software solutions with Python
• Experience with probabilistic and statistical machine learning, including Bayesian methods, Monte Carlo techniques, and uncertainty‑aware modelling
• Familiarity with scientific Python libraries like Num Py, Sci Py, and Pandas
• Familiarity with machine learning libraries such as PyTorch and scikit‑learn
• Experience with Dev Ops and MLOps
• Degree‑level qualification in computer science or related field
• Professional experience with collaborative software development
• Familiarity with Linux, bash, and the command line
• Ability to write logical, consistent, self‑explanatory code
• Understanding of software design patterns, SOLID and DRY principles, and architectural patterns
• Experience with Git/Git Hub and best practices
• Knowledge of the software testing pyramid and types of automated testing (smoke, component, unit, performance, load, end‑to‑end)
• Experience with Docker and other containerisation platforms
• Proven ability to collaborate in a fast‑paced “agile” team, preferably using “scrum”

• A Master's‑level qualification in a STEM field
• Experience deploying infrastructure as code
• Experience with UI/UX design principles
• Familiarity with normalising flows and/or variational autoencoders
• Publications in physics, engineering, or other simulation‑heavy domains

On site.

As an ambitious, rapidly‑growing start‑up, we’re looking for proactive, adaptable people who thrive in a fast‑paced environment. Our standard working hours are 9.00–5.30pm, Monday to Thursday, though some flexibility outside these hours may be required to meet…