Are you passionate about leveraging machine learning to drive innovation and create value? Do you have a vision for how machine learning can transform the semiconductor industry? Do you want to be part of a team that develops cutting-edge ML solutions for the ASML Data Analytics Platform? If so, we are looking for you!
ASML leads the worldwide development, production, and sales of high-end lithography systems for the semiconductor industry. In short, we make the machines that make computer chips, or integrated circuits. We design and build some of the most complex machines that you will ever see – and the software to run them – to develop smaller, faster, and still more affordable chips. We aim to unlock the potential of people and society by pushing technology to new limits. It is because of our machines that the world’s technology has steadily evolved. ASML employs more than 40,000 employees, has offices in the US, Asia, and Europe, and is headquartered in Veldhoven, the Netherlands.
This position requires access to controlled technology, as defined in the 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.

EDUCATION AND EXPERIENCE:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.

• 5+ years of experience in machine learning, data science, or a related field.

• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, etc.
• Proficiency in programming languages such as Python.
• Experience with Azure cloud platform is a must; knowledge of GCP is preferred.
• Knowledge of MLOps, data preprocessing, feature engineering, model evaluation, testing, and CICD techniques.
• Experience with deploying and maintaining ML models in production environments.
• Familiarity with industry-related standards and regulations e.g., GDPR, ISO 27000, etc. is a plus.
• Mentor junior team members and continuously improve the AI services and the overall competence.

SKILLS:

• A strong passion for machine learning and its applications in solving real-world problems.

• A customer-centric and value-driven mindset and approach to developing ML solutions.

• Strong analytical and problem-solving skills and the ability to make data-driven decisions.
• Excellent collaboration and communication skills and the ability to work effectively with different stakeholders and teams.
• Strong knowledge of machine learning algorithms, techniques, and best practices.
• Understanding of data architecture and data modeling concepts.
• Familiarity with agile and scrum methodologies and best practices.
• Strong familiarity with software engineering practices and Python as preferred programming language.
• Ability to drive automation for improved efficiency and scalability.

• Design, develop, and deploy machine learning models and algorithms to solve complex business problems.

• Deploy machine learning models into production environments, ensuring they are scalable, traceable and maintainable. This may involve using cloud services or on-premise solutions.

• Collaborate with data scientists, data engineers, software developers, and business stakeholders to understand requirements and deliver ML-driven solutions.
• Conduct data preprocessing, feature engineering, model development, model evaluation, and monitoring to ensure high-quality ML solutions.
• Stay up-to-date with the latest trends and technologies in machine learning and implement them as needed.
• Participate in code reviews, testing, and documentation to ensure high-quality deliverables.
• Improve, monitor and maintain deployed ML models to ensure they continue to meet performance, reliability and accuracy requirements.
• Contribute to the development of best practices and standards for machine learning within the organization, by developing CI/CD templates and standardized frameworks and the creation of guidelines and processes for Data Science teams to use MLOps capabilities.
• Develop and implement containerization strategies for machine learning applications using tools such as Docker and Kubernetes.