FAQs
What kind of projects will I be working on as a Machine Learning Engineer at ZEISS?
You will develop algorithms and support the end-to-end machine learning lifecycle, taking ideas from academic stages to prototypes across the complete ZEISS product portfolio.
Is a Ph.D. required for this position?
A Ph.D. is considered a plus, but a university degree in computer science, engineering, or a similar field is required.
What programming languages should I be proficient in for this role?
You should have a strong record of programming in Python or C++, along with solid knowledge of modern machine learning tech stacks.
Are there opportunities for collaboration with academia?
Yes, you will build an excellent network in both academia and industry to leverage the latest technology advancements.
What is the emphasis on coding practices in this role?
You will implement best practices to enhance the existing codebase and infrastructure, focusing on stability and scalability.
What skills are important for this position beyond technical proficiency?
Strong communication and presentation skills, along with a hands-on mindset, are important for success in this role.
Is experience in CI/CD solutions and cloud providers necessary?
Proven experience in CI/CD solutions, virtualization technologies, and container orchestration is preferred, with familiarity with state-of-the-art cloud providers being a plus.
Will I be part of a team in this role?
Yes, you will be integrated into a team of scientists and research engineers, working collaboratively on various projects.
How does ZEISS support knowledge exchange among its employees?
You will actively contribute to knowledge exchange within the team and the broader ZEISS machine learning community by promoting best practices.
What should I include in my application?
Please include your references with your application when applying for the position.