Machine Learning Engineer Data Strategy (m/w/d)

We are a Hamburg-based startup democratizing cancer diagnostics with AI. As a Senior Machine Learning Engineer you will be part of an interdisciplinary team and become a key driver in building highly accurate image analysis systems and machine learning models for the pathologists’ clinical routine. You will work on solutions to make the overall workflow for machine learners much smoother by making sure to have engineering excellence in the machine learning repos, making visualization tools, inference and training scripts as fast as possible and making sure that GPU training can happen across different servers.

Understand all the technology which we use for serving ML models live and re-use them for internal tools in the machine learning team

Implement efficient AI production systems in cooperation with our software developers

Use our internal APIs to gather and transform data and optimally prepare it for ML training

Support our medical team with tooling to efficiently guide the evaluation of annotators and make the annotation process more efficient

hD in Computer Science, Mathematics, Physics or similar with excellent grades

Experience working in the field of machine learning, especially using PyTorch

Multi-year practical experience developing professional software

Advanced skills in Python, bash, Git and linux tools

Ability to work in a fast changing, highly dynamic work environment with changing priorities

Curiosity to understand the medical application

Fluent in English, additional German is a plus

We pay relocation partially.

A competitive salary and benefits package

A flexible working environment, including options for remote work

Opportunities for professional development and career growth

Join our network on XING or LinkedIn! We’re a Hamburg, Germany-based start-up with the mission to democratise cancer diagnostics. We build support tools for visual diagnosis using state-of-the-art deep learning. Close cooperation in an international team

Optimizing cancer diagnostics with machine learning is challenging and requires the brightest minds.