OpenAI AI Engineer / Applied AI Engineer
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- Build production OpenAI applications with Responses API, structured outputs and native tools
- Ground and integrate agents, then engineer robust workflows around real user tasks
- Operate reliable OpenAI systems with evals, tracing, safety, latency/cost and adoption
Benchmark: OpenAI Applied AI Engineer + Codex Core Agents / AI Systems engineering signals (current Sep 2026). Market refresh: Responses API; Agents SDK; hosted tools and MCP; Codex; enterprise grounding; evals and tracing; sandboxed long-running agents; reliability, latency, cost, safety and measurable adoption.
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Build production OpenAI applications with Responses API, structured outputs and native tools
Responses API, model behavior + built-in tools
Coverage: OpenAI API / Responses API / Model Selection / Structured Outputs / Prompt & Context Engineering
Agents SDK, tool use + MCP
Coverage: Agents SDK / Tool Calling / Hosted Tools / MCP / Sandboxing / Human Approval
Ground and integrate agents, then engineer robust workflows around real user tasks
Use-case scoping, grounding + enterprise context
Coverage: System Scoping / File Search / Web Search / Code Interpreter / Retrieval / Enterprise Context
Codex + agentic software engineering
Coverage: Codex / AGENTS.md / Context Engineering / Code Review / Long-Running Tasks / Developer Workflow
Operate reliable OpenAI systems with evals, tracing, safety, latency/cost and adoption
Evals, regression + observable agent quality
Coverage: OpenAI Evals / Graders / Regression / Tracing / Failure Analysis / Task Success
Production optimization, safety + deployment tradeoffs
Coverage: Guardrails / Permissions / Latency-Cost-Quality / Rollouts / Fine-Tuning Strategy / Monitoring
Role-Readiness Proof / Capstone
Ship a production OpenAI application or agent for a consequential workflow. Start with the Responses API and structured outputs; add at least two tools through hosted tools, functions or MCP; use retrieval and enterprise context where needed; implement an Agents SDK orchestration path with explicit permissions, human approval and safe sandbox boundaries; create an eval dataset with automated graders and regression gates; trace key failure modes; instrument task success, latency, token cost and adoption; use Codex in the engineering workflow with tests and review; then run a staged launch, failure drill and rollback with an architecture/runbook package and measured business outcome.
Proof Rubric
- Demonstrate model and context judgment
- reliable structured outputs
- clean tool and MCP contracts
- grounded retrieval and access control
- eval-driven development and regression gates
- diagnosable traces
- safe permissions and guardrails
- resilient long-running execution and recovery
- latency/cost discipline
- verified Codex-assisted development
- staged rollout and rollback
- measurable task success and adoption
- and clear tradeoffs among prompts, retrieval, tools, fine-tuning, autonomy and human approval