The Role
Lead the architecture and implementation of AI systems that transform wearable and behavioral data into personalized sleep, stress, and recovery programs. Build signal interpretation, recommendation, feedback, evaluation, MLOps, and production monitoring capabilities on Python and Azure. Make technical decisions, establish engineering standards, protect sensitive health data, and collaborate with product, design, and marketing while remaining hands-on from prototyping through production.
Summary Generated by Built In
Virtusan is a Swiss based health coaching platform for sleep, stress and recovery that works with any wearable, including our own smart ring. We're hiring our first AI Tech Lead to build the intelligence behind our coach: turning biometric and behavioural signals into personalised programmes that help people sleep better and stress less. You’ll be the technical authority for AI across engineering; you make the calls on architecture and approach, set the standards others build to and stay hand-on in the code.
What you'll be doing
1) Signal interpretation: Turn wearable and in-app data into recovery, stress and sleep insights, using personal baselines and trend detection.
2) Multi-device data: Normalise metrics across vendors, weight sources by reliability, and handle missing data and device switches.
3) Recommendation engine: Personalise protocol sequences by dose and timing with classical ML (e.g. contextual bandits, sequential recommenders or RL).
4) Feedback loops: Learn from real user behaviour and biometric responses which protocols work, and for whom.
5) Evaluation: Offline metrics, online experiments and production monitoring for every model and LLM feature.
6) AI platform: Own the roadmap, training and serving pipelines, data quality and architecture on our Python and Azure stack.
7) Technical leadership: Make and own the key architecture and modelling decisions, and write them down (RFCs, ADRs etc.) so the team can build them.
8) Privacy: Make secure handling of sensitive health data part of the design from the start.
9) Collaboration: Help product, design and marketing shape AI features and understand what the models can and can't do.
Hands on: This is a builder role. You’ll be prototyping, evaluating and taking code through to production.
Requirements
* 6+ years in ML/AI engineering incl. 2+ years in a lead role, with at least one product taken from zero to production
* Python, FastAPI and production MLOps on Azure or another major cloud
* Time series, recommender systems, causal/uplift methods and strong statistics
* A/B testing, model evaluation and production monitoring
* LLMs and LangGraph (or similar) on Azure OpenAI, and knowing when not to use them
* Experience as a tech lead or staff engineer, making technical decisions that a team of engineers followed.
* Privacy-aware handling of health data
* Based in Europe and fluent in English
* Nice to have: bandits, RL or off-policy evaluation; health or wearable data; literature mining; AI in engineering workflows (coding agents, AI-assisted testing, CI).
Application process
Instead of a cover letter, please send us the answers to 2 of the following 3 questions together with your CV.
1) Tell us about an ML product you took from zero to production. What did you own, and what changed because of it?
2) You have a recommendation engine and want to know whether a protocol actually improves users' sleep. How would you measure that?
3) Tell us about a technical decision you made for a team that others disagreed with. How did you get alignment, and how did it turn out?
What you'll be doing
1) Signal interpretation: Turn wearable and in-app data into recovery, stress and sleep insights, using personal baselines and trend detection.
2) Multi-device data: Normalise metrics across vendors, weight sources by reliability, and handle missing data and device switches.
3) Recommendation engine: Personalise protocol sequences by dose and timing with classical ML (e.g. contextual bandits, sequential recommenders or RL).
4) Feedback loops: Learn from real user behaviour and biometric responses which protocols work, and for whom.
5) Evaluation: Offline metrics, online experiments and production monitoring for every model and LLM feature.
6) AI platform: Own the roadmap, training and serving pipelines, data quality and architecture on our Python and Azure stack.
7) Technical leadership: Make and own the key architecture and modelling decisions, and write them down (RFCs, ADRs etc.) so the team can build them.
8) Privacy: Make secure handling of sensitive health data part of the design from the start.
9) Collaboration: Help product, design and marketing shape AI features and understand what the models can and can't do.
Hands on: This is a builder role. You’ll be prototyping, evaluating and taking code through to production.
Requirements
* 6+ years in ML/AI engineering incl. 2+ years in a lead role, with at least one product taken from zero to production
* Python, FastAPI and production MLOps on Azure or another major cloud
* Time series, recommender systems, causal/uplift methods and strong statistics
* A/B testing, model evaluation and production monitoring
* LLMs and LangGraph (or similar) on Azure OpenAI, and knowing when not to use them
* Experience as a tech lead or staff engineer, making technical decisions that a team of engineers followed.
* Privacy-aware handling of health data
* Based in Europe and fluent in English
* Nice to have: bandits, RL or off-policy evaluation; health or wearable data; literature mining; AI in engineering workflows (coding agents, AI-assisted testing, CI).
Application process
Instead of a cover letter, please send us the answers to 2 of the following 3 questions together with your CV.
1) Tell us about an ML product you took from zero to production. What did you own, and what changed because of it?
2) You have a recommendation engine and want to know whether a protocol actually improves users' sleep. How would you measure that?
3) Tell us about a technical decision you made for a team that others disagreed with. How did you get alignment, and how did it turn out?
Skills Required
- 6+ years of experience in ML/AI engineering
- 2+ years of experience in a lead role
- Experience taking at least one product from zero to production
- Proficiency in Python
- Experience with FastAPI
- Production MLOps experience on Azure or another major cloud
- Experience with time series
- Experience with recommender systems
- Knowledge of causal or uplift methods
- Strong statistics skills
- Experience with A/B testing
- Experience with model evaluation and production monitoring
- Experience with LLMs and LangGraph or similar on Azure OpenAI
- Experience as a tech lead or staff engineer making technical decisions adopted by an engineering team
- Privacy-aware handling of health data
- Based in Europe
- Fluent in English
- Experience with bandits, reinforcement learning, or off-policy evaluation
- Experience with health or wearable data
- Experience with literature mining
- Experience using AI in engineering workflows, including coding agents, AI-assisted testing, or CI
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The Company
What We Do
Virtusan is a science-based digital health and behavioral change platform focused on human longevity and wellbeing. It utilizes AI and biometric tracking to create holistic health journeys, helping individuals and organizations improve their health, manage stress, and optimize their lives across key pillars such as sleep, nutrition, and mental fitness.
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