Senior Machine Learning Research Scientist

What could you build on top of real-time and non-invasive Glucose monitoring? That is what Spiden is all about, and we want to beat Apple at it:
Machine learning, personalized health, and predictive medicine. If these areas resonate with you, join us to work on foundational technologic and scientific challenges at Spiden. We are a Swiss MedTech venture with the vision to use state-of-the-art detection techniques to continuously monitor and learn from a wide range of vital indicators, to better manage chronic diseases, to customize critical treatments and, to improve your health.
Using proprietary optical sensors, Spiden is building a cutting-edge biomedical data generation pipeline to train medical-grade Machine Learning algorithms to infer glucose transcutaneously from spectral measurements. To achieve our vision, our team and advisory board consist of world experts coming from top academic institutions (ETH, EPFL, Columbia, Princeton, or Harvard among others) and industry leaders (Baxter, Roche, Lonza). We are looking for talented, experienced, voraciously curious, and self-driven professionals to join an A-team of doers:

After 4+ years in R&D phase, we are starting the product development phase to launch to market. Therefore, joining as one of the first 60 permanent employees provides you great opportunities for growth in responsibilities and impact.

Responsibilities
As a Sr. Machine Learning Research Scientist (Signal Processing), you will research and collaboratively build computational solutions to real world problems in our domain: a continuous non-invasive glucose monitoring smartwatch. Your work involves being able to acquire and smartly combine deep multi-disciplinary knowledge (biophotonics, biomedical, microfluidics, electronics and manufacturing). For this opening in particular, we are looking for an expert solving complex multi-spectral signal processing challenges in low SNR regimes by leveraging physically-informed ML methods.
It also involves an exposure to engineering aspects of large-scale data processing using an advanced ML technology stack.

YOUR PROFILE

Minimum Qualification

• Master's degree in Computer Science, a related technical field, or equivalent ML experience.
• 8+ years of experience in writing software working with at least one compiled and one interpreted language such as Python, JavaScript, Java, Go, C, C++
• 8+ years of Machine Learning Experience in academy or industry: developing physically-informed own ML algorithms and architectures, training models, hyperparameter tuning, feature engineering, etc
• Experience with scientific analysis packages such as NumPy, Pandas, Scikit-learn
• Experience working in Google Cloud Platform or another public cloud platform
• High attention to detail and proven ability to manage multiple, competing priorities, being comfortable in a dynamic and sometimes ambiguous environment.

Preferred qualifications

• PhD degree in Computer Science, Electrical Engineering or equivalent experience in the Machine Learning domain
• 8+ years of industry experience working with Real World Data challenges in a product that hit the market (e.g. voice assistants in production)
• Senior expertise using deep learning frameworks such as PyTorch or TensorFlow.
• Demonstrable capacity to digest scientific literature effectively, including cross-disciplinary domains.
• Exposure to MLOPs: ML data management (collect, store, manage data), creating training datasets (data labeling, data augmentation, feature engineering, data partitioning, sampling and slicing), familiarity with architectural choices for ML systems.
• Working under a matrixed organization involving cross-functional, and/or cross-business projects.
• The following qualifications are a plus:
  • Experience in the healthcare or pharma industry
  • Experience with Machine Learning at the Edge (optimization, HW accelerators, GPU, distributed computing)
  • Demonstrable expertise exercising cross-functional leadership beyond functional team

IMPORTANT: we cannot sponsor working 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 MedTech 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 Spiden, 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. Within the SMLE team we take special care of gender diversity - currently balanced at 50/50 among the permanent employees- 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. We want you to grow with Spiden.

Amazing team
In this role, you will be part of the Machine Learning Research & Engineering team, currently composed by 18 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 RnD teams, including Biomedical Science, Biochemistry, Biophotonics, and Electrical Engineering.

Don't forget to check the team page on our website

RECRUITMENT PROCESS

The interview process consists of 3 stages:

• Introduction call (20 min - led by hiring manager - remote with video)
• Technical Interview (60-90min - led by engineers from the team - remote with video)
• On-site interview min - Culture & Team fit, Lab tour, if role requires meet members from other teams - in person in our office and lab in Pfäffikon)