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ASML

Physics Machine Learning Engineer

ASML is hiring a Physics Machine Learning Engineer. Level rates it ; you can apply on ASML.

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  • Builds AI ●●●● is how Level rates this machine learning engineer role: the job is building AI systems.
  • Level lists 686 other open roles at ASML.
  • The role was posted in the last few days.

AI in this role

Physics Machine Learning Engineer

Do you enjoy solving algorithm design problems for the semiconductor metrology industry, with demanding time, accuracy, and memory requirements ? Do you like to use your creativity, your in-depth knowledge of physics principles, numerical maths and machine learning, and your hands-on experience with practical problem solving, being part of a highly talented group of algorithm experts ?

Sector Information

Within ASML the sector Development & Engineering is responsible for the development, specification and design of new ASML products. The Business Line Applications provides integrated solutions with computational, metrology and control technology. These solutions extend and improve the performance of lithography and patterning products for the semiconductor industry.

Group Mission

Within D&E Applications, the Modeling and Inference Group covers the development of optical and mathematical models and methods required to infer physical model parameters from optical metrology data. Relevant new metrics and algorithms as well as new measurement functions, with optimum performance characteristics, are identified, designed and implemented. The availability and development of fast and accurate optical models and calibrations, and the optimal inference strategy, are essential. The group secures both the ASML competencies Applied Mathematics for Parameter Estimation and Scatterometry.

Job Description

  • Development of models for scanners and optical metrology solutions with statistically correct parameter inference, deep learning and optimization algorithms, and system calibrations, to improve semiconductor high-volume fab control solutions.
  • Implement machine learning and deep learning scanner models and metrology applications, with a mindset for scalable data-intensive and distributed software architectures, at the interface with colleague physics, data science, and software groups at ASML.
  • Communicate crystal clearly on physical principles, mathematical solutions and design decisions to stakeholders, without omitting the essentials.
  • Drive for data and code quality, and collaborate and help to implement along industry coding best practices.
  • Work as a team with similar-minded people, benefitting from each other’s specific competences.
  • 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.
  • 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.
  • Keywords: Mathematical optimization, optical modeling and calibration, mathematics, inverse problem, parameter inference, (non-)convex optimization, machine learning, deep learning, neural network, regression, information theory.

Education

  • Ph.D. or MSc. in Physics, Applied Mathematics, Computer Science, or Electrical Engineering

Personal skills

  • Excellence in numerically stable modeling and inference strategies, using sound physical-mathematical principles and insights
  • Ability to explain physical principles and algorithmic solutions in a crisp way, without omitting the essentials
  • Excellence in numerical mathematics, and affinity with machine learning methods, data-intensive and distributed software architecture (cloud) as environment for metrology applications
  • Drive for structuring the scripting code base in the cluster, and be energized by helping colleagues in this
  • Fluency in the languages Python, C++, MATLAB, or Julia, 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
  • Pragmatic approach and pro-active attitude, with result focus and a ‘can do’ spirit

This position requires access to controlled technology, as defined in the United States Export Administration Regulations (15 C.F.R. § 730, et seq.). Qualified candidates must be legally authorized to access such controlled technology prior to beginning work. Business demands may require ASML to proceed with candidates who are immediately eligible to access controlled technology.

Inclusion and diversity

ASML is an Equal Opportunity Employer that values and respects the importance of a diverse and inclusive workforce. It is the policy of the company to recruit, hire, train and promote persons in all job titles without regard to race, color, religion, sex, age, national origin, veteran status, disability, sexual orientation, or gender identity. We recognize that inclusion and diversity is a driving force in the success of our company.

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How we rate this

Physics Machine Learning Engineer at ASML rates 84 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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