Reinforcement Learning Engineer

Bugcrowd is hiring a Reinforcement Learning Engineer for its RL and Reasoning Team. The role builds verifiable reinforcement learning environments and automated verification pipelines that turn software vulnerabilities into deterministic reward signals for AI models. It combines cybersecurity analysis, program execution, and scalable machine learning infrastructure.

Responsibilities:

• Design, build, and deploy high-throughput RLVR (Reinforcement Learning from Verifiable Rewards) environments that evaluate LLM action sequences against deterministic execution outcomes
• Develop automated test harnesses, sandboxes, and verification engines that convert complex vulnerability research (e.g., memory corruption, web security, logic bugs) into binary pass/fail reward signals
• Integrate Bugcrowd’s Mayhem automated analysis platform and real-world vulnerability feeds into continuous, scalable RL environment generation pipelines
• Architect safe, isolated, and highly reproducible execution environments (using Docker, BuildKit, or Nix) capable of running thousands of simultaneous agent-driven exploitation and patching trajectories
• Collaborate directly with researchers at frontier AI labs including Anthropic, OpenAI, and Cohere to define standard benchmark formats, observation spaces, and verifiable evaluation metrics for cybersecurity tasks
• Implement precise telemetry, ground-truth verification algorithms, and trajectory logging to analyze agent reasoning paths and prevent reward hacking or false positives
• Build low-level instrumentation and debugging tools to monitor memory states, process executions, and network behaviors during agent interaction cycles
• Optimize infrastructure performance and environment reset latency to support massive-scale parallel sampling and distributed RL training workflows
• Benchmark and evaluate frontier AI model performance across diverse offensive and defensive security challenges, such as automated fuzzing, exploit payload generation, and patch validation