About the job
About Bespoke Labs
Bespoke Labs is a cutting-edge applied AI research laboratory at the forefront of data and reinforcement learning (RL) environment curation. We are dedicated to training and evaluating intelligent agents through innovative methodologies.
Recently, we developed Open Thoughts, an exceptional open reasoning dataset now utilized by leading labs, enabling the training of state-of-the-art specialized models such as Bespoke-MiniChart-7B and Bespoke-MiniCheck. We have also successfully taught agents to execute multi-turn tool-calling using reinforcement learning techniques.
Bespoke Labs is strategically positioned to capture a significant market share in data curation and RL environment development.
About The Role
We are seeking a Research Engineer to integrate pioneering research with large-scale development and deployment of RL environments. This role will place you at the intersection of research and engineering, collaborating with leading labs and enterprise clients to identify their requirements and transform those insights into systematic environment creation.
The ideal candidate will possess both research acumen and execution excellence. You will need to stay abreast of the latest advancements in agent training, effectively communicate with research teams at top labs, and construct robust systems that deliver high-quality environments at scale. You should be equally comfortable with academic literature, prototyping new approaches, and deploying production pipelines.
Your collaboration will extend to external partners (frontier labs, enterprise clients) and internal teams, ensuring that our research insights are translated into impactful products that enhance the capabilities of agent training.
What You'll Do
Research & Collaboration
- Collaborate with leading AI labs to comprehend their agent training requirements and design tailored environments.
- Keep updated with the latest research in RL, agent training, and evaluation methodologies.
- Prototype innovative approaches for environment generation, curriculum design, and data curation.
- Convert academic findings into actionable engineering solutions.
Environment & Data Pipeline Development
- Design and implement scalable data pipelines for RL environments.
- Enhance existing systems for performance and reliability.
- Collaborate with cross-functional teams to deliver integrated solutions.

