# Software Engineer, Cyber Frontier

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Security Engineering
**Salary:** $230k – $325k/yr
**Skills:** Python, TypeScript, JavaScript, Machine Learning, Cybersecurity, Model Training, Security Engineering, Evaluation Systems, Safeguards, Risk Assessment
**Posted:** 2026-05-27

> Build AI-powered security products and evaluation systems that help trusted defenders respond to cyber threats while ensuring responsible deployment of frontier AI models.

## Job Description

## Responsibilities
- Define and execute the technical roadmap for Codex Cyber’s security products, including evaluations, safeguards, trusted-defender workflows, and deployment decisions
- Work with trusted defenders, customers, and partner teams to understand cyber use cases, evaluate risk, and turn feedback into product and research priorities
- Shape cyber-specific model training and access patterns, including data, evaluations, validation, and deployment criteria
- Build and validate systems for measuring cyber capabilities, monitoring misuse risk, and proving safeguards work in practice
- Collaborate with Safety/Preparedness, Research, Security, Legal, Communications, Go-to-Market, and external partners on company-wide cyber priorities
- Translate frontier cyber research into launch-ready tools, operational playbooks, and durable infrastructure for Codex and security products

## Requirements
- Strong programming skills in Python, TypeScript/JavaScript, or similar languages
- Experience building reliable systems in ambiguous, high-stakes domains
- Experience in ML systems, security products, cyber evaluations, model training, safety engineering, or trusted customer deployments
- Ability to navigate 0→1 environments and ambiguity
- Ability to move fluidly between product launches, customer and defender partnerships, model training, evaluation science, and security research
- Clear thinking about cyber capability, misuse risk, access patterns, safeguards, and deployment tradeoffs in high-stakes environments

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