In a world of immense uncertainty, digiLab 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 digiLab’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.
Summary:
Machine Learning Engineers play a key role in driving the technical aspects of our AI initiatives by developing machine learning models and supporting the delivery of solutions for client benefit. This role involves close collaboration with cross-functional teams to develop and deliver AI solutions that solve complex challenges. ML Engineers contribute to both the research and practical application of AI solutions, ensuring they meet client needs and are scalable for production environments.
The role:
As a Machine Learning Engineer at digiLab, you will be responsible for:
- Application of machine learning models to solve complex business challenges, ensuring they are production-ready and aligned with client requirements.
- Working with cross-functional teams (business development, sales, forward deployed engineering and client-facing teams) to deliver high-quality technical solutions on time.
- Working with clients to solve complex data driven problems in fields ranging from nuclear fusion to healthcare,
- Collaborating with technical and non-technical stakeholders to translate business requirements into AI solutions that meet their needs.
- Developing and applying AI workflows for use with digiLab’s central platform: the Uncertainty Engine.
- Supporting the transition of machine learning models from prototypes to production-ready systems, e.g. contributing to libraries, writing nodes and workflows to implement ML approaches.
- Assisting in the deployment and maintenance of AI models and solutions in cloud-based environments (e.g. AWS, Azure).
- Contributing to the continuous improvement and innovation of digiLab’s proprietary AI platform, the Uncertainty Engine.
- Helping to ensure that best practices in MLOps, AI/ML frameworks, and model monitoring are followed throughout the development lifecycle.
- Collaborating with senior team members to identify opportunities for growth and improvement in AI/ML capabilities.
Duties may evolve, and you may be asked to take on other reasonable responsibilities within your competence to support our growth.
Required Skills & Experience:
- A STEM degree (especially in computer science, data science, maths or a related field),
- Previous experience in machine learning or a similar technical role.
- Familiarity with statistical methods, particularly in machine learning.
- Strong programming skills in Python, with experience in AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Familiarity with cloud-based deployment of machine learning models (AWS, Azure, etc.).
- A basic understanding of MLOps practices and their application in real-world settings.
- Strong problem-solving and analytical skills, with the ability to break down complex technical challenges.
- Good communication skills and the ability to collaborate with both technical and non-technical stakeholders.
- A collaborative and proactive mindset, with an eagerness to learn and grow within the team.
Nice to Have:
- A Master’s degree or PhD in a related field.
- Experience in using probabilistic machine learning/AI.
- Previous experience in a small start-up environment.
Location:
This role is based on-site at either the Exeter or Bristol office.
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 business needs.
Our Culture and Values
At digiLab, we prioritise work-life balance with a 4-day workweek (Monday to Thursday), offering a full-time salary and three-day weekends every week! Our team is built on strong connections, with regular socials like game nights, bouldering, and paddleboarding.
We foster a culture of innovation, trust, and collaboration. Our values include:
- Creativity & Agility: Encouraging innovation and flexibility in goal achievement.
- Trust & Responsibility: Supporting each other in taking calculated risks for bold innovation.
- Open & Honest Collaboration: Ensuring transparent communication and alignment.
- High-Performance Standards: Continuously challenging ourselves to excel in delivery.
- Value-Driven Work: Regularly assessing our contributions toward company goals.
Benefits:
We value enthusiasm and loyalty, and we’re committed to offering a great work-life balance. Along with the exciting challenges this role provides, we offer a range of benefits including:
- 4-day working week
- Competitive Salary
- BUPA private health care (via salary sacrifice)
- Company Cashplan
- Cycle to work scheme
- Referral Program
- Company Events
- Discretionary EMI scheme (eligible to be considered after one year with the company; participation is not guaranteed and is entirely at the company's discretion.)
Equal Opportunities:
digiLab is an equal opportunity employer. We welcome applications from candidates of all backgrounds and are committed to ensuring our recruitment processes are fair, inclusive, and legally compliant. We take equality, dignity, and non-discrimination seriously.
Final Note:
We aim to respond to every applicant, but due to high application volumes, we may not be able to respond individually. Thank you for your interest in joining the digiLab team. The information you provide will be stored and used in line with our Privacy Notice.
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