
Metrology Machine Learning Engineer
- Veldhoven, Noord-Brabant
- Vast
- Voltijds
- Develop optical metrology solutions with statistically correct parameter inference, machine learning and optimization algorithms, and system calibrations, to improve semiconductor metrology and enable high-volume fab control solutions and Contribute to and drive the ASML competences in the group, for this position particularly focused on the machine learning side, and stay up-to-date with machine learning approaches for metrology applications.
- Implement machine learning and deep learning metrology applications, with a mindset for scalable data-intensive and distributed software architectures, at the interface with colleague data science, functional and software groups at ASML.
- Drive for data and code quality, and collaborate and help to implement along industry coding best practices and work as a team with similar-minded people, benefitting from each other’s specific competences.
- Communicate crystal clearly on physical principles, algorithm solutions and design decision to stakeholders, without omitting the essentials.
- Design and realize fully functional proof-of-concept subsystems on the edge of system specifications, costs and project planning, thereby contributing directly to products for B2B customers world-wide.
- Review technical analyses from the team, and structure team contributions keeping the overview and consolidate technical-team identity in communication with other departments.
- Contribute to technical product roadmaps and generate intellectual property protecting ASML products, while developing the best metrology solutions and a well-founded vision on semiconductor metrology.
- Ph.D. in Applied Mathematics, Physics, Computer Science, or Electrical Engineering.
- 0-3 year of working experience.
- Demonstrated expertise in machine learning and numerically stable modeling, code development, using sound physical-mathematical principles and insights
- Excellence in numerical mathematics, and in machine learning methods, data-intensive and distributed software architecture (cloud) as environment for metrology applications
- Ability to explain physical principles and algorithmic solutions in a crisp way, without omitting the essentials
- Drive for structuring the scripting code base in the cluster, and be energized by helping colleagues in this
- Fluency in the languages Python, Julia, MATLAB, or C++, and awareness of compatibility with other software
- Sound understanding of the fundamentals such as optics, linear algebra, probability theory, robust optimization and (deep) learning methods
- Drive creative solutions -within the bigger picture- with the product and customer in mind
- Initiating, self-propelling and decisive in an ambiguous environment
- Team worker, and ability to influence without power and you have a pragmatic approach and pro-active attitude, with result focus and a ‘can do’ spirit