AI Systems Engineer, Codex Agents
Builds core agent harness for Codex AI agents, enabling safe tool use, code execution, and long-horizon tasks in production. Designs systems for sandboxing, evaluation, observability, and performance optimization across ML workflows and infrastructure.
About the job
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.
Skills
Rust, Python, LLMs, Distributed Systems, Sandboxing, Inference, Gpus, Orchestration, Evals, APIs
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