About Faculty

Faculty transforms organisational performance through safe, impactful and human-led AI. 

We are Europe’s leading applied AI company, and saw its potential a decade ago - long before the current hype cycle. 

We founded in 2014 with our Fellowship programme, training academics to become commercial data scientists. 

Today, we provide over 300 global customers with industry-leading software, and bespoke AI consultancy for retail, healthcare, energy, and governmental organisations, as well as our award winning Fellowship. 

Our expertise and safety credentials are such that OpenAI asked us to be their first technical partner, helping customers deploy cutting-edge generative AI safely. 

Our high-impact work has saved lives through forecasting NHS demand during covid, produced green energy by routing boats towards the wind, slashed marketing spend by predicting customer spending habits, and kept children safe online. 

AI is an epoch-defining technology. We want people to join us who can help our customers reap its enormous benefits safely.

What You'll Be Doing

You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high-impact problems in our Government team - examples of which can be found here.

You are engineering-focused, with a keen interest and working knowledge of operationalised machine learning. You have a desire to take cutting-edge ML applications into the real world. You will develop new methodologies and champion best practices for managing AI systems deployed at scale, with regard to technical, ethical and practical requirements.

You will support both technical and non-technical stakeholders to deploy ML to solve real-world problems. To enable this, we work in cross-functional teams with representation from commercial, data science, product management and design specialities to cover all aspects of AI product delivery.

The Machine Learning Engineering team is responsible for the engineering aspects of our customer delivery projects. As a Machine Learning Engineer, you’ll be essential to helping us achieve that goal by:

• Building software and infrastructure that leverages Machine Learning;

• Creating reusable, scalable tools to enable better delivery of ML systems

• Working with our customers to help understand their needs

• Working with data scientists and engineers to develop best practices and new technologies; and

• Implementing and developing Faculty’s view on what it means to operationalise ML software.

We’re a rapidly growing organisation, so roles are dynamic and subject to change. Your role will evolve alongside business needs, but you can expect your key responsibilities to include:

• Working in cross-functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high-impact systems.

• Leading on the scope and design of projects

• Offering leadership and management to more junior engineers on the team 

• Providing technical expertise to our customers

• Technical Delivery

Who We're Looking For

At Faculty, your attitude and behaviour are just as important as your technical skill. We look for individuals who can support our values, foster our culture, and deliver for our organisation.

We like people who combine expertise and ambition with optimism -- who are interested in changing the world for the better -- and have the drive and intelligence to make it happen. If you’re the right candidate for us, you probably:

• Think scientifically, even if you’re not a scientist - you test assumptions, seek evidence and are always looking for opportunities to improve the way we do things.

• Love finding new ways to solve old problems - when it comes to your work and professional development, you don’t believe in ‘good enough’. You always seek new ways to solve old challenges.

• Are pragmatic and outcome-focused - you know how to balance the big picture with the little details and know a great idea is useless if it can’t be executed in the real world.

To succeed in this role, you’ll need the following - these are illustrative requirements and we don’t expect all applicants to have experience in everything (70% is a rough guide):

• Understanding of and interest in the full machine learning lifecycle, including deploying trained machine learning models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch

• Understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques

• Experience in Software Engineering including programming in Python.

• Technical experience of cloud architecture, security, deployment, and open-source tools. Hands-on experience required of at least one major cloud platform

• Demonstrable experience with containers and specifically Docker and Kubernetes

• Comfortable in a high-growth startup environment.

• Outstanding verbal and written communication.

• Excitement about working in a dynamic role with the autonomy and freedom you need to take ownership of problems and see them through to execution