Machine Learning Researcher, RL & Agentic Systems

About the role

DataLab is seeking a Machine Learning Researcher to focus on reinforcement learning (RL) and agentic systems. The ideal candidate will design and build datasets, tasks, and environments for benchmarking advanced AI systems.

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, trajectories, stateful environments, and verifiable outcomes that can be used to evaluate advanced AI systems.
• Develop frameworks for evaluating real-world data quality.
• Evaluate planning, tool use, robustness, recovery from failure, task completion, and generalization behavior in RL-style or agentic environments.
• Connect model failures back to concrete dataset, environment, or task-design gaps and recommend improvements grounded in empirical evidence.
• Build scalable evaluation and validation tooling.
• Partner across research, engineering, and product to identify data bottlenecks, improve evaluation methodology, and shape internal best practices around task-grounded AI training data.

Requirements

PhD or equivalent Master’s Degree + 4+ years industry experience in machine learning, computer science, statistics, engineering, mathematics, economics, or related quantitative fields.

Strong understanding of AI model training pipelines, evaluation methodology, and the role of data in shaping model performance.

Experience working with large, unstructured, or semi-structured datasets used to train or evaluate ML systems.

Experience with reinforcement learning, sequential decision-making, agentic systems, tool-using models, or multi-step model evaluation.

Experience designing tasks, benchmarks, environments, simulations, or evaluation frameworks for real-world model behavior.

Strong intuition for realism, coverage, difficulty, fidelity, and meaningful outcome structure in datasets.

Strong experimental design, evaluation, benchmarking, and data-validation skills.

High ownership and ability to independently identify and solve high-impact problems.

Qualifications

PhD or equivalent Master’s Degree + 4+ years industry experience in machine learning, computer science, statistics, engineering, mathematics, economics, or related quantitative fields.

Strong understanding of AI model training pipelines, evaluation methodology, and the role of data in shaping model performance.

Experience working with large, unstructured, or semi-structured datasets used to train or evaluate ML systems.

Experience with reinforcement learning, sequential decision-making, agentic systems, tool-using models, or multi-step model evaluation.

Experience designing tasks, benchmarks, environments, simulations, or evaluation frameworks for real-world model behavior.

Strong intuition for realism, coverage, difficulty, fidelity, and meaningful outcome structure in datasets.

Strong experimental design, evaluation, benchmarking, and data-validation skills.

High ownership and ability to independently identify and solve high-impact problems.