SW Developer / Experimental Physicist (EP-ATL-OSW-2026-121-GRAP)
Core
Research and develop machine learning (ML) and AI-based approaches for track reconstruction within the ATLAS Event Filter (EF) system for the High-Luminosity LHC.
Role type
Senior IC machine-learning engineer (particle physics)
Builds
ML-based tracking algorithms integrated into the ATLAS Event Filter workflow
Domain
High-energy physics, particle physics, scientific computing
Deliverable
production ML models
Required skills
Machine learning and deep learning frameworks, ML inference deployment, ML model training and evaluation, hyperparameter tuning, performance benchmarking, C++, Python, Git, Jira
Preferred skills
Experience with large-scale scientific software frameworks (ACTS, Athena), understanding of tracking challenges in high track density environments
Technologies
C++, Python, Git, Jira, ACTS, Athena
Responsibilities
Conduct research on ML and AI-based approaches for track reconstruction in high pile-up environments, investigate and benchmark novel ML-based tracking algorithms, contribute to studies of physics and computational performance, lead teams and define directions
Seniority
Senior, hands-on IC with team supervision
