Research Engineer​/Research Scientist - Post-training

XOR is a platform that helps world-class companies pushing the frontier of AI hire exceptional ML, RL, and AI engineering talent.

Our client is a well-funded AI startup working on next-generation training systems for large language models. The team is small, technical, and moving fast, with a strong focus on hands-on engineering over process.

This team is investigating how far self-directed learning can push model capability, and is looking for Research Engineers or Research Scientists to push the frontier of post-training on large language models - a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.

• Train and evaluate models on proprietary training tasks to validate data quality, surface gaps in task coverage, and close the feedback loop between task design and model capability
• Architect and optimize training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar - helping scale systems to handle increasingly complex research workflows
• Design, implement, and test training environments, evaluations, and methodologies for RL agents
• Profile and optimize training runs end-to-end, from data loading through reward computation, to maximize experiment throughput and shorten the research iteration cycle

• Experience running end-to-end LLM post-training pipelines
• Proficiency in Python and PyTorch or JAX
• Experience with at least one modern RL training framework
• Experience building and operating ML infrastructure at scale

• Experience evaluating model outputs and building reward or evaluation signals
• Staying current on post-training research and translating papers into running code
• Strong opinions (loosely held) about how to structure training code for reproducibility and fast iteration
• Balancing research exploration with engineering rigor
• Strong systems design and communication skills