Reinforcement Learning Engineer (Cybersecurity)

Required

• 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).
• 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.
• At Bugcrowd, we understand that diversity in the workplace is vital to a company’s success and growth. We strive to make sure that people are included and have a sense of being part of making Bugcrowd not only a great product but a great place to work.
• We regularly hear from both customers and researchers that Bugcrowd feels like a family, and we strive to maintain that internally as well.
• Our team consists of a broad range of people: musicians, adventure sports junkies, nature lovers, parents, cereal enthusiasts, night owls, cyclists, artists—you get the point.