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About the role
- Research Engineer at Bespoke Labs bridging cutting-edge research with production-scale development of RL environments. Collaborating with frontier labs and enterprise customers to design training environments.
Responsibilities
- Partner with frontier AI labs to understand their agent training needs and design custom environments.
- Stay current with latest research in RL, agent training, and evaluation methodologies.
- Prototype novel approaches to environment generation, curriculum design, and data curation.
- Translate academic insights into practical engineering solutions.
- Build and maintain scalable systems for creating, validating, and deploying RL environments
- Develop systematic approaches to data curation that ensure quality and diversity
- Create automated quality assurance pipelines for environment verification
- Design evaluation frameworks that measure environment effectiveness
- Work directly with enterprise customers to understand their specific agent training challenges
- Customize environment suites and benchmarks for different use cases and domains
- Provide technical guidance on best practices for agent training and evaluation
- Present research findings and product capabilities to technical stakeholders
- Scale research prototypes into production-ready systems that handle large-scale deployment
- Establish reproducible workflows and maintain high engineering standards
- Create documentation and tools that enable both internal teams and external users
- Monitor and optimize system performance as we scale environment production
Requirements
- MS or PhD in Machine Learning, Computer Science, or related field, OR equivalent industry research experience
- Track record of research contributions (publications, open-source projects, or deployed research systems)
- Deep understanding of reinforcement learning, agent training, or related areas
- Ability to read and implement ideas from recent papers
- Strong Python skills and experience with ML frameworks (PyTorch, JAX, or similar)
- Experience building production systems or research infrastructure at scale
- Proficiency with cloud platforms (GCP, AWS) and distributed computing
- Systematic approach to testing, validation, and quality assurance
- Excellent communication skills for working with research teams and enterprise customers
- Experience translating between research concepts and practical requirements
- Ability to scope projects, set priorities, and deliver on commitments
- Comfortable presenting technical work to diverse audiences
- Understanding of what makes research artifacts valuable to users
- Experience shipping products, datasets, or tools used by others
- Attention to detail in documentation, usability, and user experience
- Customer-focused approach to problem-solving
- Hands-on experience with RL agent training or evaluation systems (Nice to Have)
- Background in data-centric AI, synthetic data generation, or dataset creation (Nice to Have)
- Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, etc.) (Nice to Have)
- Previous experience in a research engineering or applied scientist role (Nice to Have)
- Contributions to widely-used datasets, benchmarks, or evaluation suites (Nice to Have)
Benefits
- Health coverage
- Opportunity to work directly with the world's leading AI research labs
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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