Member of Technical Staff, Research Engineering

What You'll Do7

• Architect self-contained RL environments that model complex, real-world tasks, complete with reward functions, verifiers, and evaluation logic.
• Design and scale episode pipelines and multi-component training processes (MCPs) to enable reproducible experimentation.
• Build automated data generation systems that use synthetic data to speed up training cycles while maintaining quality.
• Develop and integrate AI-driven evaluation and quality assurance systems for automated grading, validation, and feedback loops.
• Fine-tune and optimize open-source RL models using internally generated datasets and custom training strategies.
• Establish benchmarking frameworks to measure model capability, robustness, and data quality across tasks.
• Contribute to the release and analysis of evaluations on internal and external benchmark platforms such as micro1 benchmarks.

Requirements7

• Deep experience in Reinforcement Learning, including environment design and training dynamics.
• Strong track record of building and scaling RL systems, pipelines, or experimentation frameworks.
• Proficient in automation and data generation, including synthetic data pipelines.
• Familiar with automated evaluation systems, model validation, and quality assurance workflows.
• Experienced in fine-tuning and evaluating open-source ML models.
• Clear, concise communicator with strong technical writing skills.
• Comfortable operating in fast-paced, research-driven, and highly collaborative environments.

Preferred Qualifications3

• Experience publishing benchmarks, evaluations, or research artifacts.
• Familiarity with evaluation ecosystems (e.g., micro1 benchmarks or similar frameworks).
• Background in scalable infrastructure for large-scale RL experimentation.

Who Should Apply

This role suits a Research Engineer with deep RL expertise, especially in environment design and training dynamics. You have built and scaled RL systems, pipelines, or experimentation frameworks. You are also proficient in automation, synthetic data generation, and fine-tuning open-source models.

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