Senior Machine Learning Engineer

digiLab Solutions
Exeter
3 days ago
Create job alert

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

Senior Machine Learning Engineers play a key role in driving the technical aspects of our AI initiatives, leading the development of machine learning models, and guiding 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. Senior ML Engineers contribute to both the research and practical application of innovative AI solutions, ensuring they meet client needs and are scalable for production environments.


The role

As a Senior Machine Learning Engineer at digiLab, you will be responsible for:



  • Leading the development of machine learning approaches 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.
  • Developing and applying AI workflows for use with digiLab’s central product: the Uncertainty Engine.
  • Guiding the transition of AI research solutions from low TRL (Technology Readiness Level) into scalable, production‑ready systems.
  • Serving as the technical lead on client projects, providing expertise and oversight.
  • Collaborating with technical and non‑technical stakeholders to translate business requirements into AI solutions that meet their needs.
  • Ensuring best practices in MLOps, AI/ML frameworks, and cloud‑based deployment are followed throughout the development lifecycle.
  • Contributing to the innovation and improvement of digiLab's proprietary AI platform, the Uncertainty Engine.
  • Mentoring junior team members and providing technical leadership to foster their growth and development.

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, or related field).
  • Substantial industry experience in a similar role.
  • Extensive experience in AI and machine learning, with a strong background in developing and deploying models in real‑world settings.
  • Familiarity with statistical methods, particularly in machine learning.
  • Strong programming skills in Python, with hands‑on experience in AI/ML frameworks and cloud‑based deployment (e.g., AWS, Azure).
  • Experience with MLOps practices and the ability to implement robust, scalable AI systems.
  • A track record of successfully transitioning research solutions into production.
  • Excellent problem‑solving and analytical skills, with the ability to navigate complex technical challenges.
  • Strong communication skills, with the ability to articulate technical concepts to a diverse audience, including non‑technical stakeholders.
  • A collaborative mindset, capable of working effectively within a team and with external stakeholders.

Nice to Have

  • A Master’s degree or PhD in a related field.
  • Experience in using probabilistic machine learning / AI.
  • Previous experience developing individuals or teams.
  • 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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.