Machine learning engineer

ph3About LIOM /h3 pLIOM 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. /p h3Your mission /h3 pWe are looking for a talented Machine Learning Engineer with at least two years of industry experience in the tech sector. The ideal candidate is an expert in modern python and MLOps tools and practices, and has experience in translating research code into production. If you have experience developing and deploying ML models using PyTorch/Jax/TensorFlow, are familiar with tools like VertexAI and are excited about applying your skills to a challenging problem, we want to hear from you! /p pAs a Machine Learning Engineer you will /p ul liDevelop, and maintain our model, training, and evaluation code and ensure correctness, reproducibility, performance, and maintainability. /li liCollaborate with researchers and engineers to design, develop, and deploy full ML pipelines both in the cloud and on compute-constrained devices. /li liUse cloud-based ML platforms such as VertexAI to streamline and scale ML workflows. /li liEmploy MLOps tools for experiment tracking, model evaluation and selection, deployment and monitoring. /li liA passion for staying at the forefront of machine learning and software engineering and embracing the latest tools and libraries. /li /ul h3Your profile /h3 pRequired qualifications: /p ul liAt least 2 years of experience in a Machine Learning Engineering role. /li liProficiency in Python, with a strong track record of writing correct, reproducible and maintainable code. /li liProficiency in at least one of the main deep learning frameworks PyTorch/Jax/TensorFlow. /li liProficiency with MLOps tools for experimental tracking and monitoring, as well as for Hyperparameter Tuning /li liFamiliarity with cloud-based ML platforms, such as VertexAI. /li /ul pThe following qualifications are a plus: /p ul liExperience with distributed training using Kubernetes/Ray. /li liExperience with pipeline orchestration tools such as AirFlow, Prefect or Dagster. /li liExperience with model quantization and pruning. /li liExperience in a compiled or language such as C++ or Rust. /li /ul pIMPORTANT: We cannot sponsor work permits for non-EU / EFTA nationals for this role. Applications that do not fulfill this criteria will be automatically rejected. /p h3We offer /h3 pbWork/Life Balance /bbr/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. /p pbDiversity /bbr/With 18 nationalities in the company, we strive to build a diverse and exciting environment. We care about diversity and equal opportunities. /p pbMentorship Career Growth /bbr/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. /p pbAmazing team! /bbr/In this role, you will be part of a team composed of 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. /p h3Our recruitment process /h3 pThe interview process consists of three stages: /p ul liIntroduction video call /li liTechnical Interview /li liOn-site interview /li /ul /p #J-18808-Ljbffr