Field Security Specialist (cyber Security Solutions Engineer)

OpenAI OpenAI · AI Frontier · Tokyo, Japan · Go To Market

Field Security Specialist role focused on advising enterprise customers on applying OpenAI's AI models, APIs, and agentic workflows to cybersecurity use cases. The role involves understanding customer needs, demonstrating value, designing solutions, and guiding safe implementation patterns, acting as a technical advisor between CISO-level and practitioner audiences.

What you'd actually do

  1. Lead cyber workflow discovery with customers across AppSec, DevSecOps, vulnerability management, SOC/IR, detection engineering, red team, cloud security, and GRC automation.
  2. Build and deliver customer-facing demos, workshops, proofs of concept, and reference architectures for AI-enabled security workflows.
  3. Scope pilots with clear success criteria, data requirements, integrations, evaluation methods, safety boundaries, and human approval points.
  4. Advise customers on safe implementation patterns, including tool/function calling, structured outputs, sandboxing, data handling, guardrails, auditability, and approval-gated side effects.
  5. Translate between executive buyers and hands-on security practitioners, helping each audience understand value, risk, and practical next steps.

Skills

Required

  • Deep practitioner credibility across cybersecurity domains (application security, cloud security, identity, vulnerability management, secure SDLC, incident response, detection engineering, or attacker tradecraft)
  • Customer-facing, advisory, consulting, solutions engineering, security architecture, or technical field role experience
  • Ability to build credible demos or prototypes using APIs, Codex, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, or other common security tooling
  • Understanding of how to design AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, and human-in-the-loop review
  • Comfort in scoping pilots from ambiguous customer pain, including success metrics, required data, workflow integrations, evaluation criteria, deployment assumptions, and decision gates
  • Clear communication with both CISOs and hands-on practitioners
  • Strong evidence-first judgment
  • Experience building leverage for the broader field by turning one-off customer work into repeatable assets and product feedback loops

Nice to have

  • Experience with OpenAI models, APIs, Codex, and agentic workflows
  • Familiarity with secure code review, vulnerability triage, threat modeling, remediation, SOC workflows, detection engineering, and security validation

What the JD emphasized

  • partner closely with customers, Sales, Product, Engineering, and Security
  • translate frontier AI capabilities into practical workflows
  • evaluate, validate, and deploy these systems safely
  • partner with senior business stakeholders
  • guide their AI strategy
  • identify the highest value use cases and applications
  • demonstrate the value of our solutions
  • recommend architectural patterns
  • partner with our Enterprise customers
  • ensure they achieve tangible business value from our models
  • apply to real cybersecurity use cases
  • move fluidly between CISO-level conversations, practitioner-level technical depth, and hands-on solution design
  • evaluate OpenAI for workflows like secure code review, vulnerability triage, threat modeling, remediation, SOC workflows, detection engineering, and security validation
  • field’s cyber expert
  • shaping discovery, demos, pilots, reference architectures, implementation guidance, and repeatable assets
  • help customers adopt AI safely in high-stakes security environments
  • build and deliver customer-facing demos, workshops, proofs of concept, and reference architectures for AI-enabled security workflows
  • scope pilots with clear success criteria, data requirements, integrations, evaluation methods, safety boundaries, and human approval points
  • advise customers on safe implementation patterns
  • tool/function calling
  • structured outputs
  • sandboxing
  • data handling
  • guardrails
  • auditability
  • approval-gated side effects
  • translate between executive buyers and hands-on security practitioners
  • understand value, risk, and practical next steps
  • create reusable field assets
  • bring recurring customer requirements, product gaps, blockers, and high-value cyber workflows back to Product, Engineering, Security, and GTM teams
  • deep practitioner credibility across cybersecurity domains
  • customer-facing, advisory, consulting, solutions engineering, security architecture, or technical field role
  • build credible demos or prototypes using APIs, Codex, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, or other common security tooling
  • design AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, and human-in-the-loop review
  • scope pilots from ambiguous customer pain
  • success metrics
  • required data
  • workflow integrations
  • evaluation criteria
  • deployment assumptions
  • decision gates
  • communicate clearly with both CISOs and hands-on practitioners
  • strong evidence-first judgment
  • validate findings
  • separate true positives from noise
  • document assumptions
  • avoid overstating claims
  • build leverage for the broader field by turning one-off customer work into repeatable assets and product feedback loops

Other signals

  • customer-facing technical advisor
  • translate AI capabilities into practical workflows
  • guide AI strategy
  • demonstrate value of solutions
  • architectural patterns
  • apply AI models to cybersecurity use cases
  • CISO-level conversations
  • practitioner-level technical depth
  • hands-on solution design
  • evaluate OpenAI for workflows
  • build and deliver customer-facing demos, workshops, proofs of concept, and reference architectures
  • scope pilots with clear success criteria
  • advise customers on safe implementation patterns
  • tool/function calling
  • structured outputs
  • sandboxing
  • data handling
  • guardrails
  • auditability
  • approval-gated side effects
  • build credible demos or prototypes using APIs, Codex, agents, scripts, CLIs, GitHub workflows, CI/CD systems, logs, tickets, scanners, or other common security tooling
  • design AI workflows with retrieval, structured outputs, tool use, evals, guardrails, telemetry, and human-in-the-loop review
  • scope pilots from ambiguous customer pain
  • success metrics
  • required data
  • workflow integrations
  • evaluation criteria
  • deployment assumptions
  • decision gates
  • build leverage for the broader field by turning one-off customer work into repeatable assets and product feedback loops