- 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
- 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.
- 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
- Demonstrable experience of managing/ mentoring more junior members of the team
- 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
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Machine Learning Engineer - Bristol, United Kingdom - Faculty
Description
What You'll Be Doing
Working in our Defence business unit 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 space - examples of which can be found
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:
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:
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:
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):
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