Research EngineerBespoke 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.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 DoResearch & CollaborationPartner 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 DevelopmentBuild and maintain scalable systems for creating, validating, and deploying RL environmentsDevelop systematic approaches to data curation that ensure quality and diversityCreate automated quality assurance pipelines for environment verificationDesign evaluation frameworks that measure environment effectivenessCustomer EngagementWork directly with enterprise customers to understand their specific agent training challengesCustomize environment suites and benchmarks for different use cases and domainsProvide technical guidance on best practices for agent training and evaluationPresent research findings and product capabilities to technical stakeholdersProduction ExcellenceScale research prototypes into production-ready systems that handle large-scale deploymentEstablish reproducible workflows and maintain high engineering standardsCreate documentation and tools that enable both internal teams and external usersMonitor and optimize system performance as we scale environment productionWhat We're Looking ForResearch BackgroundMS or PhD in Machine Learning, Computer Science, or related field, OR equivalent industry research experienceTrack record of research contributions (publications, open-source projects, or deployed research systems)Deep understanding of reinforcement learning, agent training, or related areasAbility to read and implement ideas from recent papersTechnical ExecutionStrong Python skills and experience with ML frameworks (PyTorch, JAX, or similar)Experience building production systems or research infrastructure at scaleProficiency with cloud platforms (GCP, AWS) and distributed computingSystematic approach to testing, validation, and quality assuranceAbility to use modern tools such as Claude Code effectively.Collaboration & CommunicationExcellent communication skills for working with research teams and enterprise customersExperience translating between research concepts and practical requirementsAbility to scope projects, set priorities, and deliver on commitmentsComfortable presenting technical work to diverse audiencesProduct MindsetUnderstanding of what makes research artifacts valuable to usersExperience shipping products, datasets, or tools used by othersAttention to detail in documentation, usability, and user experienceCustomer-focused approach to problem-solvingNice to HaveHands-on experience with RL agent training or evaluation systemsBackground in data-centric AI, synthetic data generation, or dataset creationPublications in top ML/AI conferences (NeurIPS, ICML, ICLR, etc.)Previous experience in a research engineering or applied scientist roleContributions to widely-used datasets, benchmarks, or evaluation suitesLogisticsLocation: Mountain View, CACompensation: Competitive salary and equityBenefits: Health coverage, and the opportunity to work directly with the world's leading AI research labs