Collinear AI

Collinear AI

MTS - Research (Cybersecurity)

Sunnyvale, California

Sponsorship not specifiedDetected 31 days ago
JavaScriptTypeScriptPythonJavaGoC++CGitLinuxLLMsAgentic AICybersecurityPenetration TestingResearchWriting

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odds of building a lasting career here

37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Files H-1B transfers

1 transfer filing in the last year, covering 1 worker. Median labor-condition decision: 9 days. An employer that already files transfers is one that can take over an existing H-1B.

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About the role

  • We recently released CWE-bench https://cwe-bench.com/, a defensive cybersecurity benchmark with 100 held-out audit-and-patch tasks across 54 weakness types.
  • Agents must find and fix vulnerabilities in real codebases, with checks that confirm the vulnerability is resolved and existing functionality still works.
  • Deep cybersecurity expertise is enough to get started-you do not need prior AI or machine-learning experience.

Responsibilities

  • Build cybersecurity environments. Create isolated, reproducible environments with real repositories, applications, services, logs, and access controls. Make them straightforward to launch, reset, and evaluate at scale.
  • Design scenarios around vulnerability discovery and remediation, application and API security, authentication and authorization, incident investigation, and system hardening.
  • Develop reference solutions and verifiers.
  • Reproduce the underlying issue, implement a valid solution, and write checks that distinguish a real fix from a superficial workaround.
  • Contribute to benchmarks and research. Work with researchers and engineers to turn strong environments into evaluation suites and training data. Review other contributors' tasks and help document results for future releases.
  • The ability to read unfamiliar code, reproduce a security issue, understand its root cause, and implement or assess a fix.
  • Build cybersecurity environments.
  • Create isolated, reproducible environments with real repositories, applications, services, logs, and access controls.

Requirements

  • If you know how to investigate vulnerabilities, reason about security failures, and verify that a fix works, we want to hear from you.
  • You can explain why a task is realistic and why its grading is trustworthy.
  • Curiosity about applying your cybersecurity expertise to AI, and a willingness to learn how to evaluate tool-using agents.
  • Prior experience with LLMs is not expected.

Nice to have

  • Disclosed vulnerabilities, accepted security patches, strong CTF results, or useful security tools and write-ups.
  • Experience with fuzzing, static or dynamic analysis, reverse engineering, or building security labs.
  • Familiarity with CWE and OWASP classifications and how they relate to concrete software failures.
  • A repository audit where an agent must discover and repair an authorization flaw without being told where it is.
  • A small service environment where an agent investigates suspicious activity from logs and configuration, then applies and verifies a remediation.
  • An LLM application where an agent must repair a prompt-injection or tool-permission weakness while preserving legitimate functionality.
  • When applying, include a project, code sample, security write-up, or research artifact that shows how you investigate a problem and establish that your solution works.

Benefits

  • Use those findings to improve task coverage and difficulty.

Company info

  • Practical depth in at least one area of cybersecurity, such as application security, vulnerability research, penetration testing, systems security, cloud security, or incident response. Evidence can come from internships, research, open-source work, bug bounties, CTFs, or independent projects.
  • Strong programming skills in Python and at least one language used in the systems you investigate, such as C/C++, Go, Java, JavaScript/TypeScript, or Rust.
  • Comfort with Linux, Git, containers, debugging, and automated testing.
  • Clear technical writing and careful judgment about what an evaluation does and does not demonstrate. You can explain why a task is realistic and why its grading is trustworthy.
  • Curiosity about applying your cybersecurity expertise to AI, and a willingness to learn how to evaluate tool-using agents. Prior experience with LLMs is not expected.
  • Recent graduates and early-career engineers or researchers are encouraged to apply.
  • A PhD, professional certification, or previous role at an AI lab is not required.
  • We value demonstrated ability and the quality of your work.
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This listing is sourced directly from Collinear AI's careers page and normalized into a canonical job model.