Teams at ifm

Conduct and prototype novel reinforcement learning research for foundation models, including massive-scale self-play, end-to-end engineering from data curation to production, contribute to training/inference frameworks, publish and present results, engage with open-source and external collaborators.

Manage end-to-end HPC infrastructure projects for large-scale GPU clusters: coordinate vendors and internal stakeholders, track milestones and risks, maintain documentation and governance, support compliance and export-control tracking, and produce executive reports and dashboards to ensure research timelines are met.

Drive research and engineering to develop PAN world models: scalable training on large clusters, video data pipelines and annotation, evaluation benchmarks, visual tokenization, and inference optimization for real-time interaction.