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Job Description

In order to provide quick iterations and scalable machine learning solutions for current and upcoming products, you will collaborate closely with our platform and product teams as a Software Engineer in Machine Learning. We already process hundreds of cases per week for over 1,000 customers using five machine learning technologies, and you will help us continue this outstanding track record.

As part of our dynamic team, you will be:
Improving and contributing to the design and architecture of a complex set of interdependent ML pipelines.
Following MLOps best practices by maintaining and extending our CI/CD pipelines, dashboards, dev/test/prod environments, autoscaling, etc.
Working with an accomplished team of machine learning researchers and engineers that already have 8 patents on their name.
Helping researchers launch new products and help maintain the ones already used by customers.
Some initiatives on our MLOps roadmap for the next months:
1. Improving speed and robustness of multiple machine learning products

2. Continuous monitoring of incoming data in production

The 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.
Our teams are growing and so is our code base. This comes with the challenge to keep it well-designed and maintainable.
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. While in other industries failing to predict for example a recommended product has little consequence, in the medical field you don’t want to be wrong.
Job requirements
4+ years experience working with Python and Pytorch
Solid understanding of software development best practices
A passion for software and ML pipeline design concepts
The git ecosystem is your natural habitat
Know how good tests improve developer velocity
Experience with continuous deployment of live products
Experience with AWS/GCP/Azure or Kubernetes deployment
Good collaboration and communication skills
Bonus points:
Experience with experiment, model and data tracking
Knowledge of machine learning fundamentals
Worked with 3D data, like meshes and point clouds
Experience working asynchronously with a distributed team
Previous experience in medical or other regulated field