Build scalable backend and infrastructure systems for reinforcement-learning environments that train AI agents, partnering with researchers and leading AI labs. The role requires strong engineering judgment, hands-on leadership, and experience with ML/LLM concepts and scalable systems.
Build and ship high-fidelity RL environments, verifiers, and evaluation pipelines from enterprise workflow data to train and assess frontier AI models. Requires prior experience shipping environments or agentic evals plus strong full-stack engineering skills with a bias for action and detail.
Build and optimize ML models for candidate-job matching, ranking, recommendations, and marketplace allocation at Mercor. Requires production ML experience with ranking/recommendation systems and strong applied ML skills across the full stack.
Build ML models and decision systems for search, ranking, candidate-job matching, and marketplace optimization at a fast-growing AI talent platform.
Builds and deploys custom integrations, APIs, and scalable solutions for enterprise clients using Mercor's AI platform. Requires strong engineering skills in modern languages and cloud environments, with customer-facing experience.
Builds and operates scalable data pipelines and systems for post-training workflows, model evaluations, and synthetic data generation. Partners with frontier AI labs and customers, requiring strong backend skills in Python/Go/Rust and ML evaluation expertise.
Designs and maintains benchmarks, evaluation systems, and failure analysis workflows for frontier LLMs, focusing on tool use, agentic behavior, and reasoning. Builds rubrics, scorers, and dashboards while collaborating with AI research teams in a fast-paced environment.
Develops post-training pipelines, RLVR experiments, synthetic data generation, and large-scale LLM evaluation systems to enhance frontier language model performance in tool use, agentic behavior, and reasoning. Requires strong ML experience, coding skills, and research background.
Build scalable backend and infrastructure systems for reinforcement-learning environments that train AI agents, partnering with researchers and leading AI labs. The role requires strong engineering judgment, hands-on leadership, and experience with ML/LLM concepts and scalable systems.
Build and ship high-fidelity RL environments, verifiers, and evaluation pipelines from enterprise workflow data to train and assess frontier AI models. Requires prior experience shipping environments or agentic evals plus strong full-stack engineering skills with a bias for action and detail.
Build and optimize ML models for candidate-job matching, ranking, recommendations, and marketplace allocation at Mercor. Requires production ML experience with ranking/recommendation systems and strong applied ML skills across the full stack.
Build ML models and decision systems for search, ranking, candidate-job matching, and marketplace optimization at a fast-growing AI talent platform.
Builds and deploys custom integrations, APIs, and scalable solutions for enterprise clients using Mercor's AI platform. Requires strong engineering skills in modern languages and cloud environments, with customer-facing experience.
Builds and operates scalable data pipelines and systems for post-training workflows, model evaluations, and synthetic data generation. Partners with frontier AI labs and customers, requiring strong backend skills in Python/Go/Rust and ML evaluation expertise.
Designs and maintains benchmarks, evaluation systems, and failure analysis workflows for frontier LLMs, focusing on tool use, agentic behavior, and reasoning. Builds rubrics, scorers, and dashboards while collaborating with AI research teams in a fast-paced environment.
Develops post-training pipelines, RLVR experiments, synthetic data generation, and large-scale LLM evaluation systems to enhance frontier language model performance in tool use, agentic behavior, and reasoning. Requires strong ML experience, coding skills, and research background.