Senior Research Engineer Machine Learning, AI for Science

The mission of the team is to enable highly accurate and scalable predictions of the electronic structure of molecules and materials using deep learning-powered DFT. Accurate predictions of electronic structure are essential for predictive modeling across chemistry and materials science. Learn more about our work on accurate and scalable DFT in our blog and on our project website.

The primary focus of this role will be developing and maintaining efficient reliable end-to-end infrastructure for large-scale model training evaluation and benchmarking including large-scale data pipelines for heterogeneous scientific data distributed training experimentation and agentic workflows. If you have solid machine learning engineering expertise and enjoy building scalable infrastructure that enables cutting-edge machine learning research we encourage you to apply.

This post will be open until the positions are filled.

• Develop and maintain efficient reliable and scalable infrastructure for large-scale machine learning training evaluation and benchmarking.
• Architect design and implement large-scale data pipelines for heterogeneous scientific data supporting efficient model training experimentation evaluation and benchmarking.
• Build andoptimize distributed data processing model training evaluation and benchmarking pipelines.
• Develop software and agentic workflows to automate and improve model development training evaluation and benchmarking.
• Continuous monitoring and improvement of the stability of data training evaluation and benchmarking workflows
• Work cross-functionally with machine learning researchers engineers and researchers from the natural sciences.
• Maintain high standards in code quality and software design.
• Document and share practices across the organization.

Required qualifications:

• Completed MSc in computer science machine learning AI or a related area
• Proficiency in collaborative software engineering in Python
• In-depth understanding of machine learning frameworks such as PyTorch and/or Jax
• Experience in designingdeveloping and deploying ML systems
• Experience with agentic software development
• Experience building and optimizing distributed systems and large-data applications including those using tensor accelerators or GPUs.
• Ability to work in an interdisciplinary collaborative environment through effective communication of technical concepts to non-experts from different technical backgrounds

Qualifications that are considered a plus but are not strictly required:

• PhD degree in computer science machine learning AI or a related field orcomparable industry experience in working with machine learning and large datasets.
• Experience working with major cloud platforms and/or HPC
• Experience developing high-performance scientific software.
• Experience designing or developing agentic frameworks orchestration systems or infrastructure for building and running AI agents.