Machine Learning Research Scientist

Samsung R&D Center is looking for a Machine Learning Research Scientist to join us.

Samsung Israel Research Center (SIRC) is shaping the world of tomorrow, today. Focusing beyond the horizon and pushing exciting developments in many key areas of technology. Samsung is creating a new era of continuous innovation, bringing value and contribution to society and creating a workplace where our employees can enjoy making the most of their talent, creativity and passion. 

About AFSL

The Samsung Advanced Flash Solution Lab (AFSL) is part of Samsung Semiconductor Memory Business, the world’s largest manufacturer of memory and storage devices for mobile phones, personal computers, servers and data centers.

Our mission at AFSL is to augment Samsung’s memory and storage products with the newest innovative technologies, improving their capacity, performance and security.

What will you do

You will be part of AFSL’s Machine Learning Group, conducting applied research on learning-based methods for security and reliability in storage systems, with a strong emphasis on peer-reviewed publications.

You will own the full research lifecycle - from problem formulation and data analysis to algorithm design, simulations, and proof-of-concept implementation. Also, you will take part in dedicated hardware development process, working closely with hardware, system and firmware teams. You will communicate results through clear technical reports and publish your work via top-tier conferences, journals, and patent submissions.

Representative publications from the group are available for reference:

NeurIPS2023: Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability

NeurIPS2025: CLEAR: Command Level Annotated Dataset for Ransomware Detection

• Ph.D in Computer Science, Electrical Engineering or a related field
• Proven record of publications in top-tier machine learning conferences (e.g. NeurIPS, ICML)
• Experience with Reinforcement Learning, Time-Series and Tabular Data