Research Intern

About this role

About Mercor Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise. We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows. Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence. Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London. About the Role As a Research Scientist Intern at Mercor, you’ll work on research at the frontier of post-training, reinforcement learning with verifiable rewards (RLVR), data generation, and model evaluation. You’ll investigate how datasets, rewards, and training methods affect the capabilities and behavior of large language models. This may include designing controlled experiments, developing new evaluation methodologies, conducting systematic failure analysis, and testing approaches to improve tool use, agentic behavior, and real-world reasoning. You’ll work closely with research scientists, research engineers, and domain experts to turn open-ended questions into rigorous experiments. Your work will contribute to Mercor’s research agenda and may support external publications, benchmark releases, and the development of frontier AI systems. What You’ll 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 producing training and evaluation data. • Contribute to research publications, benchmark releases, and other public research outputs where appropriate. What We’re Looking For • 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 data-quality measures. • At least one publication or open source project. • 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. Why Mercor Impact: Your work powers how AI labs train and deploy their models Learning: Get early exposure to frontier AI research and engineering Growth: Work with a high-velocity team where interns ship to production 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. Mercor is an Equal Opportunity Employer and is committed to providing equal opportunity to all employees and applicants. Mercor does not discriminate based on actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression, sexual orientation, marital status, military service and veteran status, disability, protected medical condition as defined by California or local law, genetic information, or any other characteristic protected by applicable federal, California, or local laws and ordinances. Further, Mercor prohibits discrimination, harassment, and retaliation based on any combination of protected characteristics. This Policy applies to all areas of employment, including recruitment, hiring, training, promotion, compensation, benefits, discipline, termination, and all other privileges, terms, and conditions of employment. This Policy and the law prohibit employment discrimination against any employee or applicant based on any legally protected status.