Machine Learning Engineer - Life Sciences at Faculty

Faculty is at the forefront of transforming organisational performance through safe, impactful, and human-led AI. As Europe's leading applied AI company, we identified the potential of AI a decade ago, far ahead of the current hype. Founded in 2014 with our Fellowship programme, we have trained numerous academics to become commercial data scientists.

Today, we serve over 300 global customers with cutting-edge software and bespoke AI consultancy services across various sectors, including retail, healthcare, energy, and governmental organisations. We are proud of our award-winning Fellowship and our safety credentials that made OpenAI select us as their first technical partner, facilitating the safe deployment of generative AI.

AI is an epoch-defining technology, and we are looking for individuals to join us and aid our customers in harnessing its enormous benefits safely.

Your Role

As a Machine Learning Engineer at Faculty, you will design, build, and deploy production-grade software, infrastructure, and MLOps systems utilizing machine learning. Your work will help our customers solve a broad range of high-impact problems, particularly in the Life Sciences. Examples of our past work include:

• Forecasting NHS demand during the COVID-19 pandemic
• Producing green energy by routing boats towards the wind
• Reducing marketing spend by predicting customer spending habits
• Ensuring children's safety online

In this role, you will develop new methodologies and champion best practices for managing AI systems at scale while considering technical, ethical, and practical requirements. You will collaborate with both technical and non-technical stakeholders to deploy ML solutions for real-world problems.

The Machine Learning Engineering team is responsible for the engineering aspects of our customer delivery projects. Your responsibilities will include:

• Building software and infrastructure that leverages Machine Learning
• Creating reusable, scalable tools for better delivery of ML systems
• Understanding the needs of our customers
• Working with data scientists and engineers to develop best practices and new technologies
• Implementing and developing Faculty’s view on operationalizing ML software

Who We're Looking For

At Faculty, your attitude and behaviour are just as important as your technical skills. We seek individuals who support our values, foster our culture, and deliver outstanding results. Ideal candidates will:

• Think scientifically, even if they're not scientists, by testing assumptions, seeking evidence, and continuously looking for improvement opportunities
• Always find new ways to solve old problems, never settling for ‘good enough’
• Be pragmatic and outcome-focused, balancing the big picture with detailed execution

Key Requirements

To succeed in this role, you'll need the following (we don't expect all applicants to have experience in everything, but a 70% match is a good target):

• Understanding the full machine learning lifecycle, including deploying trained models developed in frameworks such as Scikit-learn, TensorFlow, or PyTorch
• Knowledge of probability and statistics, along with common supervised and unsupervised learning techniques
• Experience in Software Engineering, particularly programming in Python
• Technical experience with cloud architecture, security, deployment, and open-source tools (hands-on experience with at least one major cloud platform required)
• Experience with containers, specifically Docker and Kubernetes
• Ability to thrive in a high-growth startup environment
• Outstanding verbal and written communication skills
• Excitement about working in a dynamic role, with autonomy and freedom to own and execute problems

Join us at Faculty and be part of a rapidly growing organisation where your role will evolve alongside business needs, allowing you to contribute to technically sophisticated, high-impact projects. Apply now to make a meaningful impact with AI innovations.