6 days ago - req15478
Machine Learning Engineer
Research & development
In a nutshell
Research & development
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!
As a Machine Learning Engineer you will apply the latest technologies and tools to implement complex machine learning models. You will lead the path towards implementing solutions built by data scientist and integrate them with various Big Data platforms and architectures. You will be responsible for creating and maintaining Machine Learning pipelines that are scalable, robust, and ready for production. To achieve this, you will be closely working with domain experts, platform architects, IT specialists, software developers, data scientists and data engineers from various parts of the organization. On top of your profound technical capabilities, your success will strongly dependent on your interpersonal skills and your ability to communicate across disciplines to bridge the gap between data science and software engineering.
You will be working in a multi-disciplinary team of data scientists, data engineers, machine learning engineers, physicists, computer scientists, and engineers to build, deploy and maintain scalable Machine Learning pipelines on various Big Data platforms. The team has been developing powerful 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 data streaming solutions to serve our next-generation Analytics environment.
Master’s qualification in Software Engineering, Computer Science, Data Science or equivalent and proven Machine Learning competence.
Personal skillsd and technical skills
- Good communicator, who can connect across disciplines
- Team player, good social and team leadership skills, pro-active and customer-oriented
- Practical approach, being able to work in a high demanding, result driven environment
- Thinks creative, out of the box, self-going, fast learner
- Strong programming skills in one or more programming languages like Python, Java, or R.
- Python fluency and familiarity with popular data sciencetooling in Python, like numpy, SciPy, Pandas, etc.is a big plus.
- Proven track record with 2 years+ experience in building machine learning models using frameworks like: Scikit-learn, XGBoost, TensorFlow, Pytorch, etc.
- Familiar with Deep Learning and machine learning algorithms such as CNN, RNN, Random forest, Gradient boosting, etc .
- Familiar with relational and non-relation database systems like: MySQL, MongoDB, Cassandra, Redis, etc.
- Experience on productizing machine learning models on Cloud platforms like AWS, Azure, or GCP
Bonus points for building machine learning pipelines using containerization and orchestration technologies like Docker and Kubernetes, including:
- Apply DevOps principle to ML solutions (MLOps).
- Automate and monitor ML solutions along their lifecycle: i.e. during construction, integration, testing, releasing, deployment and infrastructure management.
- Build and operate automated CI/CD pipelines for ML solutions.
- Hands-on experience with CI/CD automation tools for ML solutions (e.g. Kubeflow, Dataiku, Argo, Airflow, MLFlow).
- Solid understanding of microservice architectural pattern with hands-on experience building and deploying Docker containers on Kubernetes.
- Hands-on experience with both code and data version control systems (e.g. Git, DVC).
- Hands-on experience working with both on-prem and cloud stacks.
Brownie points for experience building distributed data pipelines using technologies like Spark and Kafka.
Context of the position
- Within ASML, the sector Development & Engineering is responsible for the development, specification and design of new ASML products.
- Within Development & Engineering, the CSI department delivers and advances state-of-the- art methods and techniques for the structural improvement of the performance and quality of scanner components.
- The holder of this position reports to the manager of CSI Data Science & Engineering group