Reinforcement Learning Engineer - Bugcrowd

Bugcrowd, founded in 2012, is a preemptive security platform that unifies exposure discovery and assessment, offensive testing, and AI/human-insight-shaped intelligence to help organizations avoid, discover, and validate real-world risk. They empower security teams to identify critical exposures, act proactively, and prevent breaches by combining human expertise and AI.

The Role

The Bugcrowd RL and Reasoning Team is at the forefront of autonomous cybersecurity, building authentic, verifiable reinforcement learning environments for world-leading foundational AI companies. As a Reinforcement Learning Engineer specializing in Reinforcement Learning from Verifiable Rewards (RLVR), you will design and scale automated verification pipelines that transform real-world software vulnerabilities into deterministic reward functions.

This role bridges low-level security analysis with modern LLM reasoning models, engineering environments where AI agents learn to discover, exploit, and remediate software vulnerabilities with mathematical certainty. Your work will directly power rigorous, verifiable reward signals, teaching next-generation frontier AI models to master complex cybersecurity domain logic, moving beyond subjective human feedback. You will operate at the intersection of fuzzing, dynamic program analysis, system exploitation, and scalable ML infrastructure, shaping the safety and offensive/defensive capabilities of future artificial intelligence.

Essential Duties and 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.

Qualifications

Required

• 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, with Rust experience being a strong plus.
• 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).

Preferred

• 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.

Pay Range Disclosure

The national estimate for the current base range for this position is $176,400 - $242,550. This position may also be eligible to participate in a discretionary bonus program or commission plan, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Culture

At Bugcrowd, diversity in the workplace is vital to success and growth. We strive for inclusion and a sense of belonging, fostering a collaborative community. Our team is diverse, comprising musicians, adventure sports enthusiasts, nature lovers, parents, and more. We believe solving security threats requires diverse backgrounds and value perspectives from underrepresented backgrounds.