Jr. Machine Learning Engineer

Data Revenue

Date listed

2 weeks ago

Remote

Yes

Glassdoor Rating

3/5 (184 reviews)


The challenge

We are looking for talented engineers who enjoy diverse and difficult engineering challenges. To succeed, you will need to learn about bioinformatics, devops, production software development & architecture, and how to be responsible for a project and - eventually - for a team.

Together with us, you will turn cutting-edge algorithms into urgently needed software tools for metabolomics researchers - who are working to cure diseases and understand biology.

Your background
  • You have at least 2 years of software development experience and are very familiar with Python.
  • You have experience in deploying solutions to production.
  • You are an excellent communicator.
  • You studied Computer Science (not a strict requirement).

Your motivation
  • You are excited to add a scientific component to your engineering work. (Like machine learning and bioinformatics).
  • You are never satisfied with your code quality and are eager to learn more about writing clean code and designing good architectures.
  • You love taking on responsibility, working independently. Without any bureaucracy.

What you will learn
  • How to design, build, test, document and deploy scalable machine learning software.
  • How machine learning contributes to advances genetics, metabolomics and proteomics.
  • Tools to deploy production ml solutions (Docker, Kubernetes, AWS, Prefect, MLFlow, Seldon, Dask, ...)

Why it's fun
  • You will never be bored.
  • You will work with and learn from a large team of like minded engineers.
  • Contribute to open source software.
  • Go sailing together with the entire team twice a year.
  • Get shares in the company and the profit.
  • Work with a team of only software engineers - we have no business managers.

How to apply
  • Step 1: Fill out our ML Engineer questionnaire: https://datarevenue.com/apply/ml-engineer
  • Step 2: Complete a small online Code Challenge.
  • Step 3: 1st Interview with our CEO
  • Step 4: Machine Learning engineering code challenge
  • Step 5: 2nd Interview with our CTO and a senior engineer.
  • Step 6: Offer

Want to learn more about what we are working on? Checkout our blog.

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