🧭 Context
The European HPC landscape is transforming rapidly, with the emergence of Exascale systems such as the upcoming Alice Recoque supercomputer at CEA. These heterogeneous architectures require new programming paradigms to maintain performance and portability across varied GPU and CPU vendors. To address this challenge, the CExA “Moonshot” initiative contributes to key software infrastructure like Kokkos and leverages modern compiler frameworks such as MLIR (Multi-level Intermediate Representation) to establish a sustainable approach for domain experts to develop their applications without deep understanding of the modern programming models and architectures. For this purpose, we will explore the coupling technology of kokkos/python with MLIR to allow code developers to maintain kokkos codes only with the python expertise. As a case study, we will port a legacy fortran application for MDFT (Molecular Density Functional Theory) with modern programming approaches including Kokkos, Kokkos/python and python. Then, we will evaluate the performance portability, readability, productivity and mainatinability aspects of each approach to find a reasonable programming model to develop a code for domain scientists. This position is a 2 years posistion as part of CExA’s efforts to integrate advanced techniques, including JAX-based or PyTorch-based AI models, into our HPC workflows, fostering hybrid workloads that combine simulation and machine learning.
🎯 Mission
As a core member of the MDFT and CExA teams, your mission will be to develop, prototype, and evaluate tools and methodologies for performance optimization of MLIR-Kokkos-based HPC applications. A publication at highly competitive conferences such as Supercomputing is foreseen.
Your main responsibilities will include:
- 🔧 Tooling Development
- Introduce a Kokkos dialect in MLIR as an intermediate representation of Kokkos
- Implement a translation inflastructure (based on potentially existing intermediate dialects) between Kokkos and JAX / PyTorch
- 📈 Code Optimization
- Profile and optimize MDFT across multiple architectures (Nvidia GPUs, AMD CPUs/GPUs, Intel CPUs)
- Evaluate integration opportunities for AI-based models via JAX or PyTorch to support hybrid workflows
- 🤝 Collaboration & Outreach
- Collaborate with other CEA teams and international partners (Europe, US, Japan)
- Present results in conferences, workshops, and open-source contributions
🧠 Skills
You are a passionate engineer with a background in scientific computing and a drive to push the boundaries of performance portability.
Must-have:
- Experience with LLVM, or compiler infrastructure
- Proficiency in modern C++ (17 or later)
Bonus:
- Experience with performance optimization and profiling tools (e.g., VTune, Nsight)
- Familiarity with Kokkos or other GPU portability frameworks
- Familiarity with MLIR, or compiler infrastructure
- Solid software engineering practice: git, cmake, CI/CD, unit testing
- Knowledge of JAX or PyTorch and interest in ML or DL for scientific applications
💼 Salary & Benefits
- Competitive salary based on experience and qualifications
- Up to 3 remote days/week
- 75% reimbursement of public transport costs
- 5 weeks of vacation + 4 weeks RTT
- International exposure: conferences, workshops, and collaborative projects
- Health insurance, retirement savings plans, and more
📩 Apply Now
Send your CV and cover letter to yuuichi.asahi(at)cea.fr. Applications will be reviewed continuously until the position is filled.
