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Data engineer • wetzikon

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Senior Signal Processing Engineer

Senior Signal Processing Engineer

LIOM Health AGFreienbach, Schwyz, CH
Il y a 11 jours
Description de poste

About LIOM

LIOM is a Swiss deep-tech company building the ultimate health wearable for conscious living – a continuous, needle-free window into your body's chemistry, starting with glucose. Through this, we empower users to make informed lifestyle choices and live healthier and longer lives. Developed in Switzerland over 8+ years by 50+ scientists, validated across 130+ subjects, and protected by 23 patent families.


Your mission

As a Senior Signal Processing Engineer, you will work with our experimental and machine learning experts to develop new methods to optimize SNR and identifiability of our measurements by combining signal processing, system identification theory, and machine learning techniques. You will perform research and collaboratively build solutions to real-world problems in our domain: a continuous non-invasive multi-biomarker monitoring smartwatch. Your work involves acquiring and smartly combining deep multi-disciplinary knowledge (biophotonics, medicine, electronics, computational, statistics).

Your profile

Required qualifications:

  • Master's / PhD degree in Engineering, Science, or Mathematics, or a related field with a strong background in signal processing, system identification, control theory
  • 3+ years of experience applying signal processing, control theory, and system identification, preferably in low-SNR settings
  • Strong mathematical background regarding signal processing, system identification, statistics/machine learning theory
  • Demonstrable capability to digest scientific literature effectively, including cross-disciplinary domains
  • High attention to detail and proven ability to manage multiple, competing priorities, being comfortable in a dynamic and sometimes ambiguous environment.
  • 3+ years of experience in writing software in Python, and one compiled and/or typed language (e.g. C/C++, Rust)
  • 3+ years of experience in industry or applied environment


The following qualifications are a plus:

  • Experience in analyzing biomedical signals in low SNR regime (e.g. spectroscopy, EEG, EMG)
  • Experience working with PPG or IMU sensor data
  • Experience in optimal control methods (e.g. model predictive control, dynamic programming,...)
  • Experience in optics or photonics
  • Experience in software development
  • Experience with machine learning frameworks PyTorch and/or jax

IMPORTANT: We cannot sponsor work permits for non-EU / EFTA nationals for this role. Applications that do not fulfill this criteria will be automatically rejected.

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 when it comes to presence in the office ensuring the right balance between impact at work and your private 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 values 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.


Our recruitment process

The interview process consists of three stages:

  1. Introduction video call
  2. Technical Interview
  3. On-site interview