Machine Learning Research Manager

**About Liom**:
Liom is a Swiss health-tech startup dedicated to revolutionizing the field of personalized health monitoring. Our objective is to develop the first health wearable monitoring glucose non-invasively. We will launch our wearable as a consumer product in the EU in H1’2027, and then as a medically cleared product in the US. Our team consists of over 80 staff, including more than 50 PhDs, with deep expertise in entrepreneurship, product development, and the required engineering as well as scientific disciplines. We are operating out of our lab facilities in Pfäffikon SZ, Switzerland, and our office in San Francisco, United States.

**Your mission**:

- Develop innovative machine learning methods in collaboration with experts in photonics, electronics, biomedical, and fluidics, driving impactful research and development
- Lead and mentor a growing team of machine learning, data, and software engineers, fostering creativity, risk-taking, and technical excellence
- Build a strong team culture, setting clear goals, delegating tasks effectively, and motivating high performance through recognition and coaching
- Oversee team operations and budget, address challenges proactively, and create opportunities for professional growth and collaboration

**Your profile**:

- PhD in Computer Science, Statistics, Machine Learning, Applied Math, Electrical Engineering, Operations Research, Biomedical Informatics, or a publication history in such fields
- Proven work experience as a team lead of min. 3 years
- Hands-on and pragmatic leadership style
- At least 8 years of work experience, preferably in an industry characterized by machine learning for high-dimensional data analysis
- Preferably in-depth knowledge of and experience with one or more of the below:

- Optimization in the context of challenging high-dimensional objective functions
- Latent variable models including state space models
- Deep learning including deep generative models and the practical aspects of developing such models
- Identifiability of statistical models

**We offer**:
**Work/Life Balance**
Working at a growing health-tech start-up is demanding and our goals are ambitious, which is why our team puts a strong emphasis on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. Therefore, we offer flexibility in working hours and encourage you to find your own balance between your work and personal life.
Values and Mission are important at Liom, as the ultimate goal is to improve people's well-being and we aspire to live that. We will have the chance to discuss value and mission during the interview process.

**Diversity**
With 18 nationalities in the company, we strive to build a diverse and exciting environment. We care about diversity and equal opportunities.

**Mentorship & Career Growth**
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship.

**Amazing team**
In this role, you will composed by awesome colleagues from which you will have the opportunity to learn a lot professionally, but also enjoy great conversations and fresh and well informed points of view. Additionally, you will be working closely with all R&D teams, including Biomedical Science, Biochemistry, Biophotonics, and Electrical Engineering.

**Our recruitment process**:
The interview process consists of three stages:

- Introduction video call
- Technical Interview
- On-site interview

**About us**:
Liom is a Swiss health-tech venture with the vision to use state-of-the-art detection techniques to continuously monitor and learn from a wide range of vital medical indicators, allowing to improve your individual health.
- We believe that products like the ones we’re building will have an impact on a very broad set of people, and we strive to include a range of diverse perspectives on our team. We would love to hear from you_