Quin is a female-founded, digital health startup above a noodle shop 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 call it turning human experience into science.
We are looking for a Machine Learning Engineer to join our Finsbury Park based team. 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.
This is our first ML hire and your primary responsibility will be to build and deliver this capability within our team. This is a hands-on role within a collaborative, high-trust R&D environment. Our team is currently 88% #womenintech and we come from all sorts of academic and commercial backgrounds.
Quin challenges the conventional thinking and 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.
- Create on device (initially iOS) Ma chine Learning solutions that run while the user is using their phone. You’ll pair with our engineers to implement them on device which will make a huge difference to the user. Our solution is inspired by a branch of artificial intelligence called embodiment. Diabetes treatment is an uncontrolled, uncertain and rich environment with limited availability of information.
- Champion how ML fits into our world and you will be able to choose tools and processes to achieve that
- Work with our product team to select problems ideally suited to be solved using ML, define requirements, model prototypes, implement them in the app and iterate on them
- Take responsibility for Data Science across the product
- Identify, research and design algorithms in production environments to power a class one medical device that that our users trust will give them the right response
- Evaluate how algorithms perform and improve them based on business and user needs
- Work with academia (Bristol University, the Francis Crick Institute and our advisor Rolf Pfeifer) on a problem that affects millions of people
- Do the hard stuff; deliver the most complex functionality in the simplest way because you know it means the most to the user
- A desire to make a material difference to people’s lives
- Pragmatism. This is a commercial project so while you’ll work with academia, you’ll start with small, simple ML solutions that you can get into the hands of users within weeks and improve it over time
- Familiarity with Apple frameworks such as Core ML, Accelerator and BNNS or have a keen interest in learning them
- Familiarity with Signal Processing, bio-inspired computation and control systems is a plus (not a must.) Some software engineering skills, at least one high level coding language and a joy for learning new languages will go a long way
- Strong knowledge of various ML algorithms for different classification, regression, clustering, decision, recommender system algorithms
- 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, you know how to scale features and improve data to make your algorithms work better
- Exploration and R&D skills — this is greenfield ML environment so you’ll be comfortable making choices, working collaboratively in a team and setting things up from scratch
- Experience working with small data sets and an interest in sparse ML
- Enough programming experience to prototype solutions to problems (Core ML, Python, Swift — it’s all going to be on device for quite some time…)
- Desire to work in a cross-functional, small team, agile startup environment
- PhD or MSc in Machine Learning, Robotics, Electrical Engineering or related field
To apply or find out more, please contact firstname.lastname@example.org