Machine Learning Engineer, Faculty

• 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
• Knowledge of (or interest in) retail-focused solutions around understanding consumer behaviours

• You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning
• 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
• 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
• Working in cross-functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high-impact systems
• Working with senior engineers to scope projects and design systems
• Providing technical expertise to our customers
• Technical Delivery

• Hybrid working: We believe people have needs, responsibilities and interests that require something different to a strict working day. We trust people to organise and take accountability for their own work and do our best to support their lives outside Faculty. We provide you with all you need to work from home, including a laptop, keyboard, chair…even Sony headphones!
• Equity, we want you to benefit from Faculty’s growth and success
• Unlimited holidays: We encourage each other to use this time to take a break, work on personal projects, or to spend time with their friends and family
• Fantastic private health, optical and dental cover, for you & your family - including 24/7 unlimited virtual private GP appointments and covering pre-existing medical conditions
• Access to mental health coaching with Sanctus
• Lunch weekly, and more fruit, drinks and snacks than you could ever eat (for office-based employees)
• We work hard and make sure we enjoy what we do. So we have frequent socials and informal get-togethers to help make sure you enjoy your time with us. You’ll make friends and professional connections that will last a lifetime

Total funding: $53m

Faculty work with companies such as Samsung, the BBC, and Just Eat to figure out what they could do with AI, and then help them to do it through implementing the right skills, strategy and software.