Senior Machine Learning Engineer

ASML - San Diego, CA

Full-time - Senior

San Diego, CA

Machinery Manufacturing

About the position

The EUV Source team in San Diego operates in a fast-paced, uniquely innovative, and challenging environment to deliver new-to-industry products supporting the commercialization of EUV technology by ASML. The Senior Machine Learning Engineer will be part of the EUV Droplet Generator Controls team within ASML San Diego. The Droplet Generator is sometimes referred to as the "heart" of the EUV source and supplies the tin which fuels EUV generation. This requires control and optimization where we produce tin droplets 27um in diameter, over 100 m/s in speed and position them with um and ns accuracies. Due to the high complexity of the EUV light source, we, as Source Performance, are a dedicated team with the mission to validate and ensure that EUV sources meet or exceed customer performance requirements and expectations. In order to execute this mission, EUV Source Performance acts as the critical enabler of knowledge acquisition and transfer throughout the product development process, via substantive participation in the technology development and system definition process for products in definition and early development stages. This includes ensuring quality of the module to sub-system to system-level performance testing and validation for the products in the integration phase, driving technology improvements enabling new products field release, and refinement of system specifications based upon a deep understanding of factory and field performance. The role also involves the creation and delivery of analytic tooling, action plans, and procedures to drive maximum solving power of our customer support organizations for sustaining products. The Senior Machine Learning Engineer will collaborate to design and implement the end-to-end AI and data science lifecycle, from data collection and preprocessing to model deployment and monitoring, providing technical leadership and guidance. The engineer will apply theoretical models, concepts, methods, and novel experimental techniques to quantitatively interpret the performance of EUV source systems across a wide range of issues and multiple technical disciplines. Additionally, producing clear, concise technical documentation in defined company formats, presenting results, and aligning with cross-functional teams is essential. The role requires working closely with cross-functional groups including systems engineering, development engineering, scientists, IT, global customer support personnel, and manufacturing personnel to drive results in AI projects.

Responsibilities

• Participate in the technology development and system definition process for products in definition and early development stages.
• Ensure quality of the module to sub-system to system-level performance testing and validation for the products in integration phase.
• Drive technology improvements enabling new products field release and refinement of system specifications based upon deep understanding of factory and field performance.
• Create and deliver analytic tooling, action plans, and procedures to drive maximum solving power of customer support organizations for sustaining products.
• Collaborate to design and implement end-to-end AI and data science lifecycle, from data collection and preprocessing to model deployment and monitoring.
• Apply theoretical models, concepts, methods, and novel experimental techniques to quantitatively interpret performance of EUV source systems across a wide range of issues and multiple technical disciplines.
• Produce clear, concise technical documentation in defined company formats, present results and align with cross-functional teams.
• Work closely with cross-functional groups to drive results in AI projects.

Requirements

• MS or PhD in Computer Science, Data Science, or Machine Learning.
• Minimum 5 years' experience in a similar role.
• Perform verification of SDS, verify design specifications from team members and contribute to the testing process (TPS, a-test, (sub system) TAR, b-test).
• Integrate sub-systems into the system, execute configuration management, and provide support to projects.
• Structural troubleshooting capabilities on functional level and manage many technical uncertainties at sub-module level and develop creative solutions to solve uncertainties.

Nice-to-haves

• Experience in designing and implementing machine learning and deep learning architectures.
• Developing and optimizing neural network architectures using frameworks such as TensorFlow and PyTorch.
• Implementing scalable machine learning models and algorithms in a cloud-based environment.
• Experience with Natural Language Processing (NLP), Computer Vision, and other AI domains.
• Proficiency in using GPU and TPU hardware accelerators for deep learning training and inference.
• Understanding of the concept of large complex system integration and practices.
• Excellent analytical and troubleshooting skills.
• Excellent inter-personal and customer service skills, with an advanced understanding of customer relationship building.
• Excellent written and verbal communication skills.
• Ability to reduce large amounts of data, create a concise summary, and effectively report results through written and/or graphical formats.
• Ability to define and execute tasks based on project descriptions with minimum or no supervision.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Basic life insurance
• 401k plan
• Vacation leave (8 hours per month)
• Paid holidays (13 throughout the year)