Senior Engineer Product Intelligence (Materials Engineering – Chemistry & Thin Film Design)

Step into a career with ASM, where cutting edge technology meets collaborative culture.

For over 55 years ASM has been ahead of what’s next, at the forefront of innovation and what’s technologically possible. With more than 4,500 ASMers representing 70 nationalities, our people and our advanced semiconductor devices are playing a crucial role in trends such as 5G, cloud computing, AI, and autonomous driving.  But we’re more than just a tech company. We value diversity, inclusion and sustainability as we strive to make a positive impact on the world.  Our development programs help support your growth, shaping your future and pushing the boundaries of innovation to unleash potential. Senior Engineer Product Intelligence (Materials Engineering – Chemistry & Thin Film Design) We seek a Senior AI/ML Engineer to develop practical, science-grounded ML solutions for materials science and semiconductor applications.

Design and support AI models and workflows for relevant chemical systems for thin film development. Model & embedded known physics & mechanisms to ensure accurate model output and turnaround time Use of SoTA GenAI frameworks for materials science and ML tools deliver applications that acceleration R&D workflows Build data pipelines to extract required data from multi-modal data sources Collaborate with software & data science engineers to deliver reproducible pipelines, dashboards, APIs and decision-support tools for R&D and engineering teams. Mentor engineers define modelling standards and communicate across AI/ML, chemistry, materials and semiconductor teams.

PhD or MSc with strong expertise in CS, Data Science, Materials Science, Physics, Chemistry, Engineering or a related field. Hands-on experience building ML models, tools and frameworks for materials science. 6+ years’ experience in applying AI/ML or computational methods (MLIPs, DFT, MD, kMC, etc.) to complex scientific or engineering problems Hands-on experience on atomistic to feature scale simulation/modelling tools & frameworks like – generative models, deterministic models, Stochastic models, active learning, Python, PyTorch/TensorFlow/JAX/scikit-learn, VASP, Synopsys, Cadence, Silvaco, Python, NumPy, SciPy, and MLflow. Ability to translate physical, chemical and engineering challenges into practical AI/ML workflows for materials engineering AI/ML experience in semiconductor materials engineering or in relevant chemistry/materials systems Experience with computational materials modelling, simulations or scientific software frameworks.

The ideal candidate bridges advanced AI/ML engineering, materials engineering/process R&D, and eager to solve problems that make an impact to the users