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 and optimize 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 designing, developing 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, or comparable 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.