Lead Machine Learning Innovation Specialist

1 week ago

London, Greater London, United Kingdom Faculty Full time
About Faculty

Faculty transforms organisational performance through safe, impactful and human-led AI. As a pioneer in applied AI, we've been shaping the industry for over a decade.

We were founded in 2014 with our Fellowship programme, training academics to become commercial data scientists. Today, we provide over 300 global customers with industry-leading software, and bespoke AI consultancy for retail, healthcare, energy, and governmental organisations, as well as our award-winning Fellowship.

Our expertise and safety credentials are such that OpenAI asked us to be their first technical partner, helping customers deploy cutting-edge generative AI safely.

Our high-impact work has saved lives through forecasting NHS demand during covid, produced green energy by routing boats towards the wind, slashed marketing spend by predicting customer spending habits, and kept children safe online.

AI is an epoch-defining technology. We want people who can help our customers reap its enormous benefits safely.

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 Consumer and Services space.

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.

To achieve this, you'll be essential to helping us develop and implement solutions that integrate machine learning with business objectives.

  • Design and build scalable software architectures that support complex machine learning models
  • Create reusable tools to enable better delivery of ML systems
  • Work with our customers to understand their needs and develop tailored solutions
  • Develop best practices and new technologies with our data science team

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:

  • Working in cross-functional teams to deliver technically sophisticated systems
  • Providing technical expertise to our customers
  • Collaborating with senior engineers to scope projects and design systems
Who We're Looking For

We look for individuals who combine expertise and ambition with optimism. 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 professional development, you don't believe in 'good enough'
  • 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

To succeed in this role, you'll need the following:

  • Understanding of the full machine learning lifecycle, including deploying trained machine learning models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch
  • Experience in Software Engineering, including programming in Python
  • 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
What We Offer

The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.

Faculty is the professional challenge of a lifetime. You'll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.

Salary Range: £80,000 - £110,000 per annum

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