ML Engineer; m​/w

Location:

Rümlang

Workload: 80-100%
Your tasks:

Tasks To strengthen our R&D team, we are seeking a Machine Learning Engineer – Wearables & Sensor Data to design, implement, and optimise Python-based machine learning pipelines and algorithms that power our wearable sensing technology.

Main tasks:

Develop and validate machine learning algorithms to predict core body temperature and related physiological parameters based on peripheral sensor signals (e.g., heart rate, skin temperature, heat flux).Design, implement, and document standardised model training and inference pipelines to ensure reproducibility and traceability

Manage and maintain datasets and measurement databases to ensure data quality, accessibility, and consistency for model development and evaluation.

Analyse internal and customer data to assess algorithm performance, identify opportunities for improvement, and deliver clear, analytical reports and recommendations.

Provide internal consultancy and feedback to product and development teams on algorithm behaviour, feasibility, and performance to guide product enhancements and innovation.

Your profile:

Master’s degree (or equivalent) in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative discipline.

Minimum of 2 years of relevant industry experience in machine learning or data science development.

Ability to provide a public or private code repository (e.g., Git Hub, Git Lab, Bitbucket) showcasing previous Python-based projects, including data analysis, machine learning pipelines, or production-level code.

Valid swiss work permit / EU citizenship

Advanced proficiency in Python, with demonstrated experience developing high-quality, maintainable, and efficient code using libraries such as Num Py, Sci Py, scikit-learn, Tensor Flow, and PyTorch.

Strong understanding of Python software engineering best practices, including version control (Git), modular code design, testing, and documentation.

Solid foundation in time-series data analysis and signal processing, with practical Python implementation experience.

Familiarity with databases and data storage solutions, including experience with time-series databases (e.g., Influx

DB, Timescale

DB, or similar) and efficient handling of high-frequency sensor data.

Strong knowledge of modern machine learning models and architectures, training workflows, and model evaluation techniques, with a demonstrated ability to stay up to date with emerging methods, tools, and industry best practices.

Excellent analytical and problem-solving abilities, with a proven track record of translating complex problems into robust Python-driven solutions.

Strong communication skills, with the ability to present technical concepts, results, and insights clearly to both technical and non-technical audiences.

Languages:

English C1.

• Testing
• Python
• Master

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