RESEARCHER, POST-TRAINING

The role

You will lead work on model post-training including supervised fine-tuning, preference data, reinforcement learning, and reward modeling. You will set direction, run experiments, and ship into production while partnering with data, infrastructure, and engineering teams.

What you will do

• Lead post-training research including SFT, RLHF/RLAIF, RLVR, DPO, and successor methods
• Design and curate data for post-training from sourcing to quality assessment
• Build and maintain evaluation suites
• Run rigorous experiments and write up internal findings
• Scale data pipelines and the infrastructure team to scale training
• Identify and characterize failure modes and design experiments to address them
• Stay current on post-training literature

Required qualifications

• Strong track record of post-training research (SFT, RL, reward modeling) at a frontier-model lab or equivalent
• 5+ years of hands-on ML research experience
• Comfort with large-scale data curation and preference-data pipelines
• Experience designing evaluation suites for capabilities that aren't easily benchmarked
• Fluent in PyTorch or equivalent
• Comfortable at the scale of distributed training
• Strong statistical instincts
• Strong written communication

Nice to have

• PhD in ML, statistics, CS, or adjacent
• Published research at NeurIPS, ICML, ICLR, COLM, RLC, or comparable venues
• Experience with reward hacking detection, scaling reward models, or RLHF infrastructure
• Synthetic data generation experience
• Background in RL math (policy gradients, importance sampling, off-policy methods)
• Open-source contributions to post-training infrastructure

About the company

makermaker.ai is a small, well-funded team building autonomous research agents for recursive self-improvement.