Quin is a female-founded, digital health startup in Finsbury Park. We use science, engineering and design to help people with diabetes who take insulin make the best possible decisions about their self-care. We’re making an “on device” mobile app that learns how people with diabetes do their self-care and gives them personalised advice to do it better, no matter when they need it. Everyone at Quin shares a desire to put our skills to work on an unsolved problem that affects millions of people.
We’re looking for a Machine Learning Engineer to join our team. You’ll get immersed in the expanding amount of data collected from our beta users, and will use this to build more intelligent app features. Our engineering team is pragmatically agile and so that we don’t work in silos, our ML engineers apply the same principles to their work. The role is a mix of academic and commercial. You’ll work with academia (Bristol University, via our Innovate UK grant) and as part of our core engineering team.
- Design, build and evaluate machine learning experiments/prototypes
- Work as part of our engineering team to integrate machine learning into the app and investigate new sources of data
- Set things up and make informed decisions, often with incomplete information. This is greenfield ML environment so you’ll be comfortable driving forward without all of the data, relying on logic, collaboration and the scientific method, rather than your previous experience
- Start with less than perfect algorithms that you can get into the hands of users within weeks and improve them over time
- Enough programming experience to prototype solutions to problems (Core ML, Python, Swift —we collect user data in the cloud but the algorithms all run on device)
- Agile software engineering skills and at least one programming language that isn’t R
- Practical knowledge of algorithms and techniques both supervised (ML) and unsupervised (classification, clustering, recommender system, different regression techniques.)
- Core ML skills; you’re familiar with tuning models, you know how to debug and test a machine learning model, how to spot and solve overfitting and bias problems, how to scale features and improve data to make your algorithms work better
- Familiarity with Apple frameworks such as Core ML, Accelerator and BNNS or a keen interest in learning them
Quin challenges conventional thinking and the received wisdom of the medical and pharmaceutical worlds. We work with a massively diverse set of user needs, turning human experience into science. We believe the more inclusive we are, the better our product will be. To do that, we’ve created an environment where everyone can bring their whole selves to work, and we welcome applications from all.
If you would like to find out more, please email firstname.lastname@example.org