Research Intern

The role

You will work on research at the frontier of post-training, reinforcement learning with verifiable rewards, data generation, and model evaluation. You will investigate how datasets, rewards, and training methods affect the capabilities and behavior of large language models.

What you will do

• Develop and investigate research questions related to post-training, RLVR, data quality, and model evaluation.
• Design and run controlled experiments to understand how datasets, rewards, and training strategies affect model performance.
• Study reward-shaping and post-training methods, including approaches such as GRPO and DAPO.
• Develop methods for measuring data quality, usability, and performance uplift on key benchmarks.
• Design and evaluate datasets, rubrics, evaluators, and scoring frameworks for complex model capabilities.
• Conduct systematic error analysis to identify model failure modes and opportunities for improvement.
• Analyze experimental results and communicate findings through clear reports, research artifacts, and presentations.
• Build the research tooling and data pipelines needed to conduct experiments at scale.
• Collaborate with research scientists, research engineers, applied AI teams, and domain experts.
• Contribute to research publications, benchmark releases, and other public research outputs.

Required qualifications

• Currently pursuing a master’s or PhD in computer science, machine learning, statistics, mathematics, or another relevant field.
• Demonstrated ability to formulate research questions, design experiments, and draw sound conclusions from empirical results.
• Demonstrated experience in at least one of the following: training, fine-tuning, or evaluating language models; Agentic AI system, RL environments; developing benchmarks, evaluation methodologies, or
• Strong programming skills, particularly in Python, and the ability to write reliable research code.
• Familiarity with machine learning fundamentals, experimental design, and statistical analysis.
• Intellectual curiosity.
• Comfort operating in a fast-paced research environment with rapid iteration and a high degree of ownership.

Nice to have

• Previous research experience in language models, reinforcement learning, model evaluation, or post-training.
• Experience training, fine-tuning, or evaluating language models.
• Familiarity with RLVR techniques, reward modeling, or agentic AI systems.
• Experience developing benchmarks, evaluation methodologies, or data-quality measures.
• Research publications or submissions at competitive CS conferences such as ACL, NeurIPS, ICML, ICLR, or EMNLP.
• Research papers, technical reports, open-source projects, or other work samples demonstrating relevant skills.

Benefits

• Mentorship from experienced researchers.
• Work on real, high-impact projects.
• $1.5K monthly stipend for meals
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement
• Free Equinox membership
• Team events and offsites.
• Potential full-time return offer.

About the company

Mercor builds infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. The company is profitable and recently raised a $750 million Series D at a $20 billion valuation.