Senior Machine Learning Engineer (100% Senior/Principal)

MLOps,Infrastructure,Cloud,Artificial Intelligence,Software engineeringWhat 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: https://t.ly/krj7d
 
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: https://t.ly/vmCKi

After 4+ years in R&D phase, we are sprinting towards 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 Engineer you will build ML Engineering infrastructure needed to deploy a portfolio of ML models and algorithms on our smartwatch and companion mobile Apps. To be effective in this role, you need to effectively interface with Science (ML Scientists, Data Scientists), Product Development (Electrical Engineering, Photonics, Manufacturing) and Product Management, to acquire and smartly embed multi-disciplinary knowledge into our ML Infrastructure.

You will be joining the Machine Learning Research & Engineering team, currently composed by 20 awesome colleagues. You will be working hand-in-hand with an A-team of 3 Software Engineering leaders, coming from the domains of Data, Cloud and App/Web development with a combined 40YOE in tech, plus an amazing and enthusiastic crew of junior engineers.Qualifications

• 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++
• 6+ years of Machine Learning Experience in industry developing and deploying ML models in real-world applications.
• Proficiency in software development methodologies, version control systems (e.g., Git), and CI/CD pipelines is essential.
• In-depth understanding of MLOps principles and best practices, including model versioning, continuous integration and deployment of machine learning models, model monitoring, automated retraining, scalability and resource optimization. Experience with MLOps tools such as Kubeflow, MLflow.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes) and infrastructure-as-code tools (e.g., Terraform) is beneficial.
• High attention to detail and proven ability to manage multiple, competing priorities, being comfortable in a dynamic and sometimes ambiguous environment.
• The following qualifications are a plus:
  • Experience with distributed computing platforms (e.g., Spark) and cloud-based machine learning services (e.g., AWS SageMaker, Google Cloud Vertex AI ) 
  • Experience with Machine Learning at the Edge (optimization, HW accelerators, GPU, distributed computing) 
  • Compliance and Governance: Understanding of compliance requirements and governance frameworks relevant to machine learning deployments, such as data privacy regulations (e.g., GDPR, HIPAA) and medical device industry-specific standards would be a plus

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.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 20 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!The interview process consists of 3 stages:

1. Introduction call (20 min - led by hiring manager - remote with video)
2. Technical Interview (60-90min - led by engineers from the team - remote with video)
3. On-site interview (90 - 120 min - Culture & Team fit, Lab tour, if role requires meet members from other teams - in person in our office and lab in Pfäffikon)