Reinforcement Learning Infrastructure (Cybersecurity)

Bugcrowd

5d • $176,400 - $242,550 • Remote

About The Position

We are Bugcrowd. Since 2012, we’ve been empowering organizations to take back control and stay ahead of threat actors by uniting the collective ingenuity and expertise of our customers and trusted alliance of elite hackers, with our patented data and AI-powered Security Knowledge Platform™. Our network of hackers brings diverse expertise to uncover hidden weaknesses, adapting swiftly to evolving threats, even against zero-day exploits. With unmatched scalability and adaptability, our data and AI-driven CrowdMatch™ technology in our platform finds the perfect talent for your unique fight. We aim to create a new era of modern crowdsourced security that outpaces threat actors. Unleash the ingenuity of the hacker community with Bugcrowd, visit www.bugcrowd.com. Based in San Francisco and New Hampshire, Bugcrowd is supported by General Catalyst, Rally Ventures, Costanoa Ventures, and others.
Job Summary
The Bugcrowd RL and Reasoning Team focuses on pushing the boundaries of autonomous cybersecurity by building authentic reinforcement learning environments for foundational model companies. As a Staff Engineer, you will advance the frontier of AI Reinforcement Learning development and delivery. You will build the infrastructure and tooling that transforms real-world vulnerability research into large-scale reinforcement learning environments used to train next-generation AI systems.
This role is unique. You will help create the training environments that teach AI systems how to hack and defend software. Your work will directly influence the capabilities of the next generation of AI models. Instead of building a single application, you will build the infrastructure that generates thousands of environments used to train frontier AI systems.
Our team works at the intersection of AI, security research, and systems engineering, building environments that allow models to learn skills such as vulnerability discovery, exploitation, and remediation.
Essential Duties and Responsibilities
If you enjoy building high-performance systems that power cutting-edge AI research, this role is for you.
This role focuses on building the systems that generate RL environments, not just the environments themselves. You will design pipelines that ingest software projects, analyze them with Bugcrowd’s Mayhem platform, and automatically construct training environments used by frontier AI labs including Anthropic, OpenAI, and Cohere.
The ideal candidate is a strong systems engineer who understands:
Reinforcement learning workflows
Building clean, reproducible Linux ML environments (containers, MCP, etc)
System security background in binary exploitation, such as buffer overflows, fuzzing, exploitation, and x86/64.
Experience developing applications in Python and C, with Rust a plus.

Requirements

• Reinforcement learning workflows
• Building clean, reproducible Linux ML environments (containers, MCP, etc)
• System security background in binary exploitation, such as buffer overflows, fuzzing, exploitation, and x86/64.
• Experience developing applications in Python and C, with Rust a plus.
• Understanding of RL training workflows used by modern LLM systems
• Experience with DevOps pipelines (e.g., github actions), reproducible builds (docker, buildkit, nix).
• Proficiency in Python and C.
• Understanding of software vulnerabilities, fuzzing, or program analysis
• Experience with build systems and large open-source codebases
• Comfort working with Linux systems and low-level debugging

Nice To Haves

• Other languages (especially Rust) are a plus.
• Experience working with benchmark environments (CTFs, SWE-bench, security challenges, etc.) is a plus

Responsibilities

• design pipelines that ingest software projects
• analyze them with Bugcrowd’s Mayhem platform
• automatically construct training environments used by frontier AI labs including Anthropic, OpenAI, and Cohere

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed