Updated 19h ago

Senior ML Engineer at SkinVision · AI Job Signal

For this role, we will only consider candidates who are currently based in the Netherlands.

About SkinVision

Skin cancer is highly treatable when caught early, but limited access to specialist care and long wait times delay diagnosis, increasing the risk of late-stage cases and driving up treatment costs. SkinVision, founded in Amsterdam in 2012, exists to close that gap. Our proprietary AI — certified as a Class IIa medical device under EU regulation, with over 90% sensitivity and 89% specificity — has already helped over 3 million people worldwide. Today, SkinVision has a database of more than 6 million skin cancer images and can reach 20 million people through insurer partnerships. Where needed, cases are reviewed by a team of dermatologists, who support users in taking the right next step — from urgent care to a specialist referral. We are extending this further, connecting users faster to the specialist care they need. We're a focused, international team, active globally, united by one goal: making early detection accessible to everyone, everywhere. If you join us, you're not just building a product — you're helping close the gap between a person noticing something and getting the care they need.

Senior Machine Learning Engineer at SkinVision

We're on the lookout for a Senior Machine Learning Engineer based in our Amsterdam office with the right skills and experience to ensure the Machine Learning team releases models with exceptional performance making use of state-of-the-art techniques. Not only are ML models vital to our product and hence for our end users, e.g., enabling early detection of skin cancer, they also enable our internal team to perform optimally. The ideal candidate possesses strong expertise in training new and optimizing existing Machine Learning and computer vision models (Python), has hands-on experience in MLOps and deploying models to production (preferably in AWS) and is able to gather requirements and communicate results to the stakeholders directly involved in the model development and deployment. Moreover, in this senior role, it is vital that you can mentor other team members, own the technical processes and actively contribute to the ML roadmap. Knowledge of data orchestration tools (Apache Airflow), data modeling (dbt) and working in a regulated and/or medical environment are a plus!

What you will do

Drive the full ML model development lifecycle: from understanding and co-defining the problem to providing inputs on data collection, assessing the quality of data, model training, validation, deployment and monitoring.
Create requirements for data collection for both new and existing models. This includes data for model training and testing.
Develop and improve efficient ML training pipelines in the cloud (AWS or similar).
Involve stakeholders appropriately during the entire development, deployment and monitoring lifecycle.
Scale the ML environment and processes to multiple models being developed and deployed simultaneously.
Ensure ML development aligns with medical device standards if applicable, including ML best practices, experiment tracking, traceability, data lineage and documentation.
Support and mentor team members.
Showcase ML models and capabilities both internally and externally, by giving presentations, workshops, training, etc.
Stay abreast of the latest industry standards, best practices and emerging trends in Machine Learning and AI and share insights with the broader team.

Your profile

8-10 years of proven professional experience as a Machine Learning Engineer or similar role, showcasing exceptional proficiency in model training, deployment within a consumer product, data analysis, stakeholder management and teamwork/mentoring.
Bachelor's or Master's degree in Data Science, Econometrics, Statistics, Computer Science or a related field.

Skills

Ability to translate user needs and requirements into technical requirements and a development plan.
Hands-on expertise with modern deep learning architectures and other ML architectures, ideally but not restricted to image data.
Hands-on experience with MLOps practices for scalable training, deployment and monitoring.
Experience in developing and deploying ML models for a consumer product (mature development processes), ideally in the healthcare space or a regulated environment.
Ability to iterate quickly from ideas to experiments, learning from results and making data-driven decisions.
Strong analytical mindset, problem-solving skills and the ability to extract actionable insights from intricate datasets.
Excellent communication and presentation skills, with the capacity to effectively convey technical concepts to diverse audiences.
Proven ability to thrive in a collaborative, cross-functional team environment, managing multiple priorities and deadlines.
Experience with mentoring and leading team members.

What we offer you