Faculty is an applied AI company that helps organisations who have the scale, data, and foresight to adopt AI into their business. We're helping make AI real across society by providing a unique combination of strategy, software and skills to our customers: everything needed to successfully create value from AI. Founder-led and with over 80 PhDs, we're a team of specialists with experience across healthcare, finance, government, retail, engineering, construction and a host of other sectors.
We believe that AI should be trustworthy, impactful and beneficial across society. Those principles have shaped our work with more than 230 organisations across the public and private sectors as we help them use AI to act faster, make better decisions and understand more deeply. Thanks to our dedicated AI safety research programme, we're constantly refining our systems to make AI safer, more secure and more reliable.
*About the role:*
You will design, build, and deploy production grade software and infrastructure to support machine learning applications & MLOps processes in a wide variety of client settings. We have a wide-ranging set of clients and projects covering: counter terrorism, government bodies, retail, marketing, healthcare, built environment, and financial services.
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 from the lab into the real world. You will develop new methodologies and opinionated best practices for managing AI systems deployed at scale, with regards 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 and data science specialities to cover all aspects of AI product delivery.
The Machine Learning Engineering team is responsible for the engineering aspects of our client delivery projects. As a Senior Machine Learning Engineer, you'll be essential to helping us achieve that goal by:
* Designing and building software and infrastructure to support Machine Learning applications;
* Leading engineering interactions with our clients;
* Mentoring data scientists and engineers in best practices and new technologies;
* Creating reusable, scalable tools to enable better delivery of ML systems; and
* 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:
* Scoping projects
* Providing technical support during Business Development
* Managing and Mentoring technical staff
* Technical Delivery
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:
* 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 your 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.
Now for the practical part - 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 all these (70% is a rough guide):
* Understanding of and interest in the full machine learning lifecycle
* Understanding of the core concepts of probability and statistics
* Familiarity with common supervised and unsupervised learning techniques
* A track record of deploying trained machine learning models developed using common frameworks such as scikit-learn, TensorFlow, or PyTorch
* Package and module design
* Application development
* Testing frameworks such as nose or pytest
Linux system management:
* Shell scripting
* Package management
* System provisioning with tools such as as Puppet, Chef, or Ansible
Docker / Containerisation:
* Container building
* Local development workflows
* Productionisation and deployment
* Orchestration tools such as Nomad or Kubernetes
* Creating QA / build pipelines
* Managing build artifacts
* GitOps software versioning and release
* Creating useful automated tests
* Understanding of best practices when testing
Use of a cloud provider:
* Experience deploying software to cloud environments such as AWS, GCP or Azure
* Experience creating & debugging infrastructure for deployed applications, including: Databases, Network stacks; and Container & serverless technologies.
Systems design and maintenance:
* Have designed, built and maintained distributed applications.
* Have added features to existing applications
* Have designed data pipelines including: Storage technology usage, Versioning, Retrieval, Cleaning; and Audit.
* Working knowledge of Terraform, or another infrastructure as code system.
* Experienced with integration testing in representative environments.
* Experience building & running software developed in a compiled language
* Excellent communication skills
* Experience in working directly with clients and end users
* Requirements gathering
* Technical planning and scoping
* User acceptance testing
* A drive to learn new technologies and novel application of existing technologies
* Experience in leading technical delivery
* Technical mentoring
* A learning environment: Faculty is dedicated to growing and learning. This translates into dedicated learning time every Friday morning, pair programming with a more experienced engineer, frequent lunch and learns, and plenty of tech talks.
* Genuinely flexible working: We believe people have needs, responsibilities and interests that require something different to a strict 9-6 working day. We trust people to organise and take accountability for their own work and do our best to support their lives outside Faculty.
* Unlimited holidays
* Cycle to work
* £100 Work from Home equipment fund for any work-essential tech or home office equipment (on top of your laptop, of course)
* Access to TechScheme monthly personal tech repayment process
* Sanctus mental health
* Telephone call
* Coding Test
* Technical Interview
* Final Interview
Machine Learning, Python, Linux, Docker, CI/CDMachine Learning, Python, Linux, Docker, CI/CD, Automated Testing, Terraform