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Bespoke Labs

Backend Engineer

Mountain View (Remote)fulltimemidAdded 1 month ago

About this role

Bespoke Labs seeks an Infrastructure Engineer to design and own the execution layer for RL training environments, enabling agents to operate coherently across multi-tool systems for extended periods. You'll build production systems for sandboxing, state management, performance optimization, and observability to support long-horizon agent training at scale.

What you'll do

  • Design sandboxing and execution infrastructure with snapshot/restore capabilities for environment state management across long agent rollouts
  • Develop failure detection and recovery systems to identify issues early and revert environments to known-good states
  • Optimize platform performance, throughput, latency, and cost-per-rollout across thousands of concurrent long-running environments
  • Build frameworks for specifying, packaging, and deploying RL environments used internally and by external customers
  • Create debugging and observability tooling for researchers to analyze failures across hundreds of agent traces
  • Scale prototypes into production systems with high engineering standards and comprehensive documentation

What they're looking for

  • Distributed systems and execution engine design
  • Container and sandboxing infrastructure (namespaces, cgroups, VMs, gVisor, Firecracker)
  • Profiling, scheduling, and resource utilization optimization
  • Cloud platforms (GCP, AWS)
  • Python programming
  • Systems languages (Rust, Go, or C++)
  • Filesystem and process state management
  • Testing, validation, and reliability engineering
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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.

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Likely interview questions

  • Walk us through your experience building or operating production systems at scale—what was the biggest bottleneck you encountered and how did you tackle it?
  • Tell us about a time you had to design a sandboxing or isolation layer (containers, VMs, namespaces, etc.). What trade-offs did you make between security, performance, and complexity?
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