OpenAI는 Codex Core Agents 팀에서 에이전트 하네스를 설계하고 운영할 AI 시스템 엔지니어를 찾습니다. Rust 및 Python을 활용한 분산 시스템 구축 경험과 LLM 에이전트 및 샌드박싱 기술에 대한 깊은 이해가 필수입니다. 연구와 프로덕션 사이의 가교 역할을 하며 에이전트의 신뢰성과 성능을 최적화하는 핵심 업무를 수행합니다.
About The Team
The Codex Core Agents team builds the agent harness that turns model capability into real-world action. We own the systems around the model: prompting and interpreting model outputs, executing actions safely in real environments, and feeding production experience back into better models and better agent behavior.
This team sits close to research and works across the stack: harness, model interaction, inference, sandboxed execution, orchestration, evals, production reliability, and the performance envelope around tokens, latency, cost, capacity, and quality. The harness is open source and increasingly part of how models are trained and evaluated, making this one of the highest-leverage layers in Codex.
About The Role
We’re looking for engineers to build the AI systems that make Codex agents dependable in production. The ideal candidate is an agent-systems builder: hands-on across low-level systems and ML workflows, able to debug Codex behavior end to end across the harness, model behavior, inference/runtime stack, GPU fleet, and product surface.
You’ll work with research, infrastructure, and product to design agent harness capabilities, run experiments and ablations across the model + system prompt + harness stack, build frameworks for assessing production agent performance, and turn messy failures into durable improvements.
What You’ll Do
Design and build the core agent harness and execution loop that lets Codex agents interpret model outputs, use tools, execute code, and complete long-horizon tasks safely.
Build sandboxing, isolation, orchestration, state, and workflow infrastructure for agents operating in real development environments.
Develop evaluation, experimentation, and debugging systems that distinguish harness issues, model behavior, inference/runtime issues, and product failures.
Run ablations across prompts, model-facing interfaces, context construction, tool-use strategies, and harness behavior to improve solve rate, reliability, latency, and cost.
Improve observability, profiling, and diagnostics across the agent stack, from backend systems to inference, GPUs, and fleet capacity.
Work closely with research to make the harness trainable, measurable, and useful for improving frontier agentic models.
Build shared primitives that make Codex faster, safer, more reliable, and easier for other teams and open-source users to build on.
You Might Be A Good Fit If You
Have built or operated production systems in distributed systems, infrastructure, developer tooling, sandboxing, virtualization, cloud platforms, or ML systems.
Enjoy working across layers: Rust systems code, Python configuration layers, APIs, agent orchestration, evals, logs/traces, inference behavior, runtime constraints, and user outcomes.
Have hands-on experience with LLM applications, coding agents, evals, model deployment, inference, compiler/runtime performance, or developer platforms.
Care deeply about reliability, safety, performance, debuggability, and clean abstractions.
Can debug from evidence and move quickly from ambiguous production failures to practical, durable fixes.
Want to work close to research while still shipping changes to production
Still write meaningful code, show strong ownership, and can lead scoped or multi-team AI systems work.
Bonus Points
Deep Rust, systems, sandboxing, isolation, or low-level platform experience.
Experience with coding agents, agent harnesses, tool-using LLM systems, model evals, or post-training feedback loops.
Background in compilers, kernels, runtimes, inference optimization, GPU systems, benchmarking, profiling, or performance engineering.
Experience building production infrastructure used by many engineers or users under demanding reliability and security constraints.
Open-source infrastructure or developer-platform work with strong taste for APIs and usability.
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