Research Scientist - Environments, Data and Post-Training

Job summary

Responsibilities

As a Research Scientist at Mercor, you will work at the intersection of research and engineering on frontier post-training. You will develop new training and evaluation methods, test them through rigorous experiments, and implement successful approaches at scale.

• Implement novel post-training methods that improve model reasoning, tool use, and agentic behavior.
• Develop new training recipes for frontier open models.
• Design and run experiments across datasets, reward functions, environments, and optimization strategies, including methods such as GRPO and DAPO.
• Build reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at scale.
• Investigate model capabilities and failure modes, then develop targeted training interventions.

What they're looking for

• Demonstrated experience training and evaluating machine learning models.
• A strong research record in post-training, reinforcement learning, language-model evaluation, data-centric ML, or a closely related field.
• Ability to reason rigorously about model behavior, experimental results, and data quality.
• Strong programming skills and experience implementing machine learning systems.
• Knowledge of the current AI research landscape and important open problems.