Introduction to the job

Data science is a broad and a demanding domain, and typically data scientists come from different background than software engineers. Most of the time, a data scientist is more focused on translating the business problem to a data science problem, and has less bandwidth to focus on all other aspects required to productize the machine learning model. As you can imagine, productizing a machine learning model comes with its own challenges like efficient coding, automation, monitoring, and scaling. This is where you come in. As a machine learning engineer at ASML, you will write efficient code to implement complex machine learning models and nurture them towards production to support the operation of the most complex lithography machines in the world.

You will be working in a multi-disciplinary team of data scientists, functional experts, data engineers, machine learning engineers, physicists, computer scientists, and IT engineers to build, deploy and maintain scalable Machine Learning pipelines on various Big Data platforms. The team has been developing powerful predictive and diagnostic software tools that are aiming to uncover causal relationships between complex lithography equipment parameters and system performance. In addition, the team is engaging on predictive solutions for condition-based maintenance and equipment health monitoring.

Role and responsibilities

An important aspect of the role is to build and deploy scalable machine learning pipelines using containerization and orchestration technologies like Docker and Kubernetes.

In return we provide:

Education and experience

At least University Bachelor’s qualification in Software Engineering, Computer Science, Data Science or equivalent and proven Machine Learning Operations (MLOps) competence.

Experience:

Skills

Additional qualifications for Predictive Maintenance development team:

Proven track record in designing, experimenting and building accurate and scalable Machine Learning models Ability to create fast prototypes – experiment and iterate towards ideal solution in an Agile/ CRISP-DM manner Experience in creating Kubeflow and Airflow pipelines (+++) Well versed with the latest GCP offerings like Vertex AI, BigQuery, dataprep Pub/Sub, DataProc (+++) and experience with Feature Store Ensuring data is available in the right format and reliable quality for other DS and ML colleagues. Working on improvements of the quality of data as well as challenges as data shift and concept drift Testing Data engineering pipelines at staging environments and deploying them at production Writing proprietary packages / frameworks to be used for internal purposes to make the standard tasks easier (such as active learning, MLOps2, exploratory data analysis, etc.) Nice to have: hands-on experience in working with ASML machine and performance data sets Nice to have: good knowledge and experience with database engineering, building and using optimally databases

Other information

Within ASML, the sector Development & Engineering is responsible for the development, specification and design of new ASML products. 

Within Development & Engineering, the DATA (DAta infrastructure, diagnostics Tooling and Analytics) department delivers and advances state-of-the- art methods and techniques for the monitoring, diagnostics and structural improvement of the performance and quality of scanner components using scanner data.

The holder of this position reports to the manager of Predictive Maintenance & Diagnostics group.

EOE AA M/F/Veteran/Disability

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Diversity and inclusion

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 diversity and inclusion is a driving force in the success of our company.

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