Machine Learning Engineer

About Faculty

At Faculty, we transform organisational performance through safe, impactful and human-centric AI.

With a decade of experience, we provide over 300 global customers with software, bespoke AI consultancy, and Fellows from our award-winning Fellowship programme.

Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI.

Should you join us, you'll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.

We operate a hybrid way of working, meaning that you'll split your time across client location, Faculty's Old Street office and working from home depending on the needs of the project. For this role, you can expect to be client-side for up to three days per week at times and working either from home or our Old Street office for the rest of your time.

About the Role

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 Defence team - examples of which can be found here.

Because of the potential to work with our clients in the National Security space, you will need to be eligible for Security Clearance, details of which are outlined when you click through to apply.

What You'll Be Doing

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.

Our Machine Learning Engineers are 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:

1. Building software and infrastructure that leverages Machine Learning;
2. Creating reusable, scalable tools to enable better delivery of ML systems;
3. Working with our customers to help understand their needs;
4. Working with data scientists and engineers to develop best practices and new technologies;
5. Implementing and developing Faculty's view on what it means to operationalise ML software.

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

1. Working in cross-functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high-impact systems;
2. Working with senior engineers to scope projects and design systems;
3. Providing technical expertise to our customers;
4. Technical Delivery.

Who We're Looking For

You can view our company principles here. We look for individuals who share these principles and our excitement to help our customers reap the rewards of AI responsibly.

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:

1. 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;
2. 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;
3. 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):

1. Understanding of, and experience with the full machine learning lifecycle;
2. Working with Data Scientists to deploy trained machine learning models into production environments;
3. Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch;
4. Experience with software engineering best practices and developing applications in Python;
5. Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud providers (AWS, GCP or Azure);
6. Demonstrable experience with containers and specifically Docker and Kubernetes;
7. An understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques;
8. Demonstrable experience of managing/mentoring more junior members of the team;
9. Outstanding verbal and written communication;
10. 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.

What we can offer you:

The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.

Faculty is the professional challenge of a lifetime. You'll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.

Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you'll learn something new from everyone you meet.