Research Engineer (Machine Learning)
Join us to make an impact for healthcare
In a world where access to clinical support is waning due to the growing shortage of doctors and outdated healthcare systems, we believe that everybody should have access to the care they need when it comes to the health of their skin. To help democratise healthcare, and build a more efficient patient journey, we develop technologies that leverage the power of dermatological AI to make high quality and effective healthcare available from the smartphone in your pocket.
We are looking for a new colleague to help drive our smartphone AI technology in medical imaging forward. You will be part of a team of like-minded research engineers with years of experience in building pipelines for machine learning and data science. You will also join the effort to develop a state-of-the-art machine learning pipeline, where great software engineering and algorithmic understanding is crucial. Task review and quality assurance is part of our daily teamwork. We expect that you thrive and excel under a great deal of freedom, while being able to adapt to ever-changing needs.
The ideal candidate
We are looking for individuals who are eager to make an impact for healthcare and want to contribute to projects that have the potential to help millions of people.
As our ideal candidate, you…
- Are excited about designing performant solutions that meet product needs.
- Hold an MSc in computer science or related discipline.
- Have at least 2 years work experience with machine learning, either writing ML code or building the infrastructure to support the running of such code.
- Hold strong programming and computer science skills, including experience with Python and version control.
- Have a disciplined approach to testing and quality assurance.
- Have employed (and have opinions on) development principles such as continuous integration, automated testing, serverless infrastructure and agile.
It is a bonus if you have…
- Experience working on data pipelines at scale.
- Worked with go-to-market machine learning development.
- A history of open source contributions or challenge participation in e.g. ISIC, ISBI, SPIE, Kaggle.
- Experience with Spark, AWS, Github, CircleCI.
- A PhD degree within computer science (or related discipline), preferably within topics of machine learning, algorithms, or software engineering.
- Publications in relevant research areas.
What we offer
- A unique opportunity to work in the heart of Copenhagen with an international team where leading edge research is turned into real impact on the lives of people suffering from skin diseases.
- Ability to attend scientific conferences and professional training.
- Competitive salary, pension contribution, and premium health insurance.
- Flexible holidays and an extra week after 9 months, so you can take time off when you need to.
- Paid parental leave to let you spend valuable time with your loved ones.
- Employee discounts for a range of activities and shops.
- We have an on-site chef to serve lunch every weekday. We also have team breakfasts every Friday, and if you are running low in the afternoon, you can fuel up with a selection of snacks.
- MacBooks and super-ultra-wide screens as your productivity base – unless you prefer otherwise.
How to apply
Apply by sending your resume and motivational letter to firstname.lastname@example.org with the subject “Research Engineer (Machine Learning)”. We will invite applicants for interviews on a rolling basis.
Please include all relevant academic and industry credentials and link to your portfolio/Github.
About LEO Innovation Lab
LEO Innovation Lab was established in 2015 as an independent innovation unit of Denmark’s oldest pharmaceutical company LEO Pharma. Being owned by a foundation, LEO Pharma has no shareholders to consider, and all profits are reinvested into new research to help patients. The innovation lab functions with a high degree of autonomy to address global healthcare challenges with digital technology designed to alleviate specific pain points experienced by patients or doctors within the ecosystem of skin diseases. We collaborate with research partners from across the world, including the USA, Europe, and China, to achieve global impact for healthcare.