Reinforcement Learning Engineer

Overview

Design and scale automated, verifiable reinforcement learning environments that convert real-world software vulnerabilities into deterministic reward signals for training frontier AI models on cybersecurity tasks.

What you'll do

• 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 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.

What you'll need

• Understanding of RL training workflows used by modern LLM systems, specifically execution-based feedback or Reinforcement Learning from Verifiable Rewards (RLVR).
• Proficiency developing applications in Python and low-level systems programming in C.
• Solid understanding of software vulnerabilities, binary exploitation, fuzzing methodologies, or program analysis.
• Experience with DevOps pipelines (e.g., GitHub Actions), reproducible builds (Docker, BuildKit, Nix), and comfort working with Linux systems and low-level debugging.
• Experience working with or building benchmark environments (e.g., CTFs, SWE-bench, security challenges, or execution sandboxes).

Nice to have

• Rust experience (a strong plus).
• Experience designing custom reward functions, ground-truth verifiers, or automated grading engines for AI safety and reasoning models.
• Background in low-level program analysis tools, sanitizers (e.g., ASan/MSan), compiler instrumentation, or automated exploit generation tools.
• Proven track record of participating in or developing competitive cybersecurity benchmarks, CTFs, or open-source AI evaluation frameworks.

Details

• Location: Remote, US (work-from-home 100% of the time).
• Physical requirements: sitting and/or standing, remaining stationary 50% of the time; carrying/moving a laptop as needed.
• Base salary range: $176,400 - $242,550 (US national estimate), with possible eligibility for a discretionary bonus or commission plan.
• Position has access to highly confidential information; background checks will be conducted.