Did you know that your teeth are like your fingerprints: they’re uniquely yours? If you need a replacement tooth for one that you lost, it will also need to be uniquely designed to fit you. Promaton is changing dental healthcare by automating diagnostics and treatment workflows using AI, making healthcare more affordable and accessible for everyone. See our company page to learn more about what we do.
As an experienced MLOps engineer, you will be working closely with our platform and product teams, enabling fast iterations and scalable ML solutions.
Making our AI pipelines work smoothly by solving complex problems
Taking a leading role in executing company-wide initiatives on our MLOps roadmap
Proactively helping with setting the right prioritization to ensure work is aligned with the business needs, while keeping tech debt in mind
Coaching other team members and sharing knowledge to help the team improve alongside you
Working with an accomplished team of machine learning researchers and engineers, who already have 8 patents on their name.
Some examples of initiatives on our MLOps roadmap for the next months:
1. Deployment architecture improvements to reduce cost and latency
2. Continuous monitoring of incoming data in production
Challenges you’ll face:
All our products process large data points, ranging from 5 up to 700MB. This means that a lot of off-the-shelf tooling doesn't work for us, and we need to invent our own to process and manage our data.
We are a cloud-native company, and cloud-native ML frameworks are still in their early days. You'll help us navigate the ever-evolving ecosystem of tools, to make sure we find the right balance between off-the-shelf and home-grown tooling.
Medical AI has to comply with the highest quality standards and we operate in a regulated environment.
5+ years of experience working with Python
Solid experience in building scalable solutions to bring AI Models to production
Experience with iterations on running products, experiment management, and data versioning
Experience in designing complex pipelines, applying SWE design principles
Solid experience with Kubernetes deployment, and preferred AWS environment
Good collaboration and communication skills
Based on a time zone between UTC-1 and UTC+3 (+/-2 hours Amsterdam time)
Experience working asynchronously with a distributed team
Bachelor/ Master level degree in Computer Science
Experience with machine learning model architectures
Practical experience with C++
Proven experience with application/microservice development
Previous experience in medical or other regulated fields
Experience productionizing computer vision models
Sounds like you? Let's talk!