Research Engineer
About Bespoke Labs
Bespoke Labs is an applied AI research lab pioneering data and RL environment curation for training and evaluating agents.
Recently, we curated Open Thoughts, one of the best open reasoning datasets used by multiple frontier labs, trained SOTA specialized models such as Bespoke-MiniChart-7B and Bespoke-MiniCheck, and taught agents to do multi-turn tool-calling with reinforcement learning.
Bespoke is uniquely positioned to capture a large market share of data and RL environment curation.
About The Role
We're looking for a Research Engineer to bridge cutting-edge research with production-scale development and deployment of RL environments. You'll work at the intersection of research and engineering—collaborating with frontier labs and enterprise customers to understand their needs, then translating those insights into systematic environment creation.
This role requires both research depth and execution excellence. You'll need to understand the latest advances in agent training, communicate effectively with research teams at top labs, and build robust systems that deliver high-quality environments at scale. You're equally comfortable reading papers, prototyping novel approaches, and shipping production pipelines.
You'll work closely with both external collaborators (frontier labs, enterprise partners) and internal teams to ensure our research insights translate into valuable products that advance the state of agent training.
What You'll Do
Research & Collaboration
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.
Environment & Data Pipeline Development
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
Customer Engagement
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
Production Excellence
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
What We're Looking For
Research Background
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
Technical Execution
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
Ability to use modern tools such as Claude Code effectively.
Collaboration & Communication
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
Product Mindset
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
Nice to Have
Hands-on experience with RL agent training or evaluation systems
Background in data-centric AI, synthetic data generation, or dataset creation
Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, etc.)
Previous experience in a research engineering or applied scientist role
Contributions to widely-used datasets, benchmarks, or evaluation suites
Logistics
Location: Mountain View, CA
Compensation: Competitive salary and equity
Benefits: Health coverage, and the opportunity to work directly with the world's leading AI research labs