Build and scale teams. Coach and develop high-potential engineers. Establish strong ownership, culture, and execution standards. Shape the team: define hiring processes, establish engineering practices, and scale a high-performing team from the ground up.
Labs use it to decide what to train next, and enterprises use it to decide which models and agents to trust. It runs evals at scale, grades every result and explains why a model fails.
Mercor · 1mo ago
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.
Some of the most valuable work on Mercor's platform is code —the tasks, problems, and solutions that train and evaluate the world's frontier coding models.
Project leads at Mercor run and scale distinct data production businesses, each hiring and upskilling thousands of experts, while managing throughput and quality.
The Applied AI org builds the systems that turn human expertise into training data for frontier models, task pipelines, expert workflows, evaluation infrastructure, and the services that tie them together.
The Applied AI org builds the systems that turn human expertise into training data for frontier models, task pipelines, expert workflows, evaluation infrastructure, and the services that tie them together.
We’re looking for a Research Scientist to lead the design of the next generation of APEX benchmarks and the expert-built datasets behind them.
As a Research Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll own benchmarking pipelines, evaluation systems, and failure analysis workflows that directly inform how we train and improve frontier language models.
Frontier AI companies are increasingly bottlenecked on expert judgment — capturing it reliably, validating it at scale, and turning it into durable model behavior. This role sits at the center of that problem.
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San Francisco, USA
GB