Machine Learning Engineer
Do you want to build an innovative product with a positive climate impact? Then Toqua is the right place for you.
Toqua is a fast-growing, award-winning AI start-up based in Ghent, Belgium. By bringing the fuel-saving potential of Machine Learning to the shipping industry we reduce emissions at an industrial scale. Our goal is to leverage the potential of cutting-edge Machine Learning to have the largest positive climate impact possible. That’s what drives us every day. To deliver better technology, at a larger scale, to reduce emissions and be part of the change our planet needs.
Toqua consists of a young & dynamic team that values ideas over hierarchy. We’re convinced work should be a place of permanent learning that lifts up yourself and those around you. Most importantly, we believe job satisfaction comes from working on something you care about.
Toqua is looking for an ambitious individual to take ownership over the MLOps. By building reliable, secure and scalable pipelines you ensure Toqua’s ML is ready to scale and reach its true potential. Furthermore, you’re in charge of keeping the ML development process explicit and clear throughout the organization, while understanding the different trade-offs to be made in the modeling approach.
- In charge of scaling up ML Operations
- Deliver maintainable, robust and testable code
- Collaborate closely with the ML team and ensure knowledge is made explicit throughout the organization
- You will help define our technical culture and contribute to building a fast-growing team
- You have relevant experience in going beyond running local ML experiments and can bring ML to production at scale. You have a track record that proves this.
- You have experience in Python (TensorFlow/Keras/sklearn/PyTorch/…) and know your way around cloud platforms (preferably GCP).
- Strong theoretical fundamentals in Machine Learning
- You can write production-grade code
- Ready to take ownership and lead
What we offer
- Work in an ambitious & motivating environment
- A fast learning trajectory and lots of ownership
- An informal and supportive way of working
- Flexible working hours, remote working options and competitive pay
- The chance to join a start-up in an early phase and share in the financial success of what you build via stock options