Faculty

Full-stack Engineer

• Work closely with delivery teams responsible for visualising insights from data scientists
• Deliver solutions to Professional & Financial Services customers using AI and Machine Learning
• Create reliable, reusable and scalable frontend and backend to enable better delivery of AI systems
• Lead engineers in cross-functional teams of Data Scientists, Product Managers and Commercial staff to deliver technically sophisticated, high-impact systems
• Lead on the architecture and design of projects
• Offer leadership and management to more junior engineers on the team
• Be the go-to authority for engineering across the stack within your team
• Provide technical expertise to customers

Software Engineer

Machine Learning Engineer

About Faculty: At Faculty, we transform organisational performance through safe, impactful and human-centric AI.
With more than a decade of experience, we provide over 350 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.
About the Role: Situated within our Applied AI consultancy; work across UK Defence, Government, Life Sciences, Energy, Banking and Retail; work may move between business areas depending on client requirements; need to be versatile and client-facing.
Requires eligibility for SC clearance and willingness to work up to three days per week on site with UK Defence customers; may require travel outside London base
What You'll Be Doing: Design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning.
Develop new methodologies and champion best practices for managing AI systems deployed at scale, supporting technical and non-technical stakeholders to deploy ML.
Responsible for engineering aspects of customer delivery projects: building software and infrastructure leveraging ML, creating reusable scalable tools, working with customers, working with data scientists and engineers, implementing Faculty’s view on operationalising ML software.
Key responsibilities include working in cross-functional teams, scoping projects/designing systems with senior engineers, providing technical expertise to customers, technical delivery

Senior Machine Learning Engineer

At Faculty, we transform organisational performance through safe, impactful and human-centric AI.
With more than a decade of experience, we provide over 350 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.
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 Defence and National Security arena.
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.

• At Faculty, we transform organisational performance through safe, impactful and human-centric AI.
• 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 Defence and National Security arena.
• 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.