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.

WHO WE’RE LOOKING FOR

At Faculty, your attitude and behaviour are just as important as your skills and experience.
Our principles guide our day-to-day actions and we look for individuals who can demonstrate their alignment with these.

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
• Technical experience of cloud architecture, security, deployment, and open-source tools
• 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
• Have experience in working directly with clients and end users to conduct: Requirements Gathering, Technical Planning and Scoping
• Technical experience of cloud architecture, security, networking, deployment, and open-source tools ideally with one of the 3 major cloud providers (AWS, GPS or Azure)
• Experience with software engineering best practices and developing applications in Python.

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 focus to make it happen. If you’re a good fit for Faculty, you probably:

• 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.
• 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.
• 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.

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 the Energy Transition and Environment space - 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 Senior 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 and mentoring data scientists and engineers to develop best practices and new technologies to deliver technically sophisticated, high-impact systems
• 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:

• 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: Work with cross-functional teams of engineers (Frontend & Cloud), data scientists, product designers and managers to deliver ML systems
• Translate user research outcomes into full system architecture that leverages Machine Learning
• Build software and infrastructure that leverages Machine Learning, and see it through to production

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
• Technical experience of cloud architecture, security, deployment, and open-source tools
• 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
• Have experience in working directly with clients and end users to conduct: Requirements Gathering, Technical Planning and Scoping
• Technical experience of cloud architecture, security, networking, deployment, and open-source tools ideally with one of the 3 major cloud providers (AWS, GPS or Azure)
• Experience with software engineering best practices and developing applications in Python