Machine Learning Researcher

Responsibilities

• Design and build datasets, tasks, and environments for benchmarking agentic systems and multi-step model behavior.
• Translate real-world workflows into structured tasks, interaction traces, and stateful environments.
• Develop frameworks to assess the diversity, realism, and downstream usefulness of datasets for agentic systems.
• Evaluate model planning, tool use, robustness, and task completion in RL-style or agentic settings.
• Build scalable tooling to automate dataset validation, environment generation, and evaluation workflows.
• Partner with research and engineering teams to identify data bottlenecks and improve evaluation methodologies.

Requirements

• PhD or equivalent Master’s Degree with 4+ years of industry experience in ML, CS, statistics, or a related quantitative field.
• Strong understanding of AI model training pipelines and evaluation methodology.
• Experience with reinforcement learning, sequential decision-making, or agentic systems.
• Experience working with large, unstructured, or semi-structured datasets.
• Proven expertise in experimental design, benchmarking, and data validation.
• Strong ability to independently identify and solve high-impact problems in an ambiguous environment.

Preferred Qualifications

• Experience with RLHF, RLAIF, imitation learning, or reward modeling.
• Experience translating real-world workflows into structured tasks or simulations.
• Experience with synthetic data generation or trajectory generation.
• Publications or open-source contributions in reinforcement learning, agents, or data-centric AI.
• Familiarity with agent evaluation frameworks like Harbor.

About the Company

Protege is building a platform to solve the biggest unmet need in AI: access to high-quality training data. We facilitate the secure, efficient, and privacy-centric exchange of AI training data to power the next generation of frontier models. We are a lean, fast-moving team of builders obsessed with velocity and impact.