Bespoke Labs
Research Engineer
About this role
Bespoke Labs seeks a Research Engineer to develop RL environments and data curation systems that bridge cutting-edge research with production deployment. You'll collaborate with frontier AI labs and enterprise customers to design custom training environments while building scalable pipelines that translate research innovations into practical solutions.
What you'll do
- Partner with frontier labs to understand agent training needs and design custom RL environments
- Build and maintain scalable systems for creating, validating, and deploying RL environments at scale
- Develop data curation approaches and automated quality assurance pipelines for environment verification
- Work directly with enterprise customers to customize environments and provide technical guidance on agent training
- Scale research prototypes into production-ready systems with reproducible workflows and monitoring
- Stay current with latest research in RL and agent training to inform systematic environment generation approaches
What they're looking for
- Reinforcement learning and agent training expertise
- Python and ML frameworks (PyTorch, JAX)
- Production systems and research infrastructure development
- Cloud platforms (GCP, AWS) and distributed computing
- Data curation and quality assurance methodologies
- Technical communication with research teams and stakeholders
- Reading and implementing recent research papers
- Testing, validation, and systematic problem-solving
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Bespoke Labs
Bespoke Labs builds specialized AI agents and reinforcement learning platforms that enable enterprises and researchers to develop, train, and deploy advanced AI systems at scale. The company is hiring AI engineers, product engineers, infrastructure engineers, full stack engineers, and research engineers to develop agent capabilities, build accessible RL platforms, and create production systems for complex agentic workflows.
View all jobs at Bespoke LabsLikely interview questions
- Walk us through a research project where you translated an academic paper or novel idea into a production system. What were the key challenges in moving from prototype to scale?
- Describe your experience with reinforcement learning—what RL algorithms or agent training approaches are you most familiar with, and how have you applied them?