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.

Provide overnight operational coverage for large-scale GPU clusters: monitor health and performance, triage incidents, support researchers, run recovery procedures, validate deployments and upgrades, track utilization, and build automation and monitoring tools.

Lead research and engineering to improve data-centric LLM reasoning, retrieval, and interaction. Develop scalable data collection and curation pipelines, build tooling for fine-tuning, evaluation, and deployment, and collaborate on RAG, agentic systems, and knowledge retrieval. Publish results, prototype APIs/infrastructure, and drive engineering best practices in a high-performance research environment.