Algorithms Engineer for ML
In this unique role, you will join Nova's Machine Learning (ML) group. You will research and develop new physical-based solutions and advanced machine learning algorithms for next-generation Optical Critical Dimension hardware and applications, as part of a world-class team.
Our goal is to transform and optimize device manufacturing and process control in the semiconductor industry. We implement Machine Learning algorithms into products deployed on Big Data ecosystems and on-edge devices to solve our customer manufacturing challenges.
- Nova provides insights into process control in the world’s most technologically advanced industry. We employ physics, math, algorithms, software and hardware expertise to redefine the limits of possible in semiconductors’ manufacturing.
- We invite you to join our dreamers and winners! Brilliant high- aimers who see impossible as the starting point to exciting challenges, and work together in multidisciplinary global teams to find answers.
- We dive deep, into the nanometric and atomic levels, to extract unique insights and provide our customers and partners with crucial decision-making data. Each and every one of us helps redefine what people can achieve through technology.
- Designing and implementing machine learning algorithm and flows for state-of-the-art application Maintaining, testing, and analyzing existing components
- Hands-on development in Python & C++/C#
- Work closely with R&D, HW, SW, and application engineers.
To succeed in this role, you should have the following skills and experience:
- M.Sc. in Computer Science /Math/ Physics/Electrical Engineering - Mandatory
- PhD– advantage
- Proven ability to develop and implement algorithms independently
- At least 2+ years experience as an algorithm engineer
- At least 2+ years experience as a Machine Learning algorithm engineer
- Strong Python & C++/C# implementation skills
- Experience with TensorFlow/PyTorch framework
- Experience with Linux environment - advantage
- Excellent communication skills and team player
- Ability to thrive in a multi-disciplinary environment
- Good team member
- Self-learning capabilities
- Highly motivated