AI Systems Engineer, Codex Agents

AI Systems Engineer - Codex Core Agents

Responsibilities

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. • 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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