Member of the Technical Staff

Poetiq is hiring technical staff to advance recursive self-improvement (RSI) at the frontier of AI research. The role spans designing algorithms for system-level self-improvement, developing reasoning strategies and dynamic execution harnesses, and building high-performance infrastructure for autonomous, large-scale LLM research and optimization.

Responsibilities:

• Improve the RSI loop — design and build the core algorithms that drive system-level self-improvement
• Push reasoning strategy — propose and test novel, task-specific reasoning strategies; probe LLMs to surface latent knowledge; build data-efficient methods for finding optimal strategies per task
• Build dynamic harnesses — synthesize task-specific execution environments on the fly, even as the system concurrently rewrites its own codebase
• Architect orchestration — design clean abstractions for highly parallelized workflows, turning fragmented signal from thousands of parallel, asynchronous LLM interactions into coherent, high-performance reasoning chains
• Probe capability boundaries — build diagnostic pipelines that separate genuine knowledge gaps from tooling limits (e.g., live retrieval) and reasoning/composition failures
• Close the feedback loop — route knowledge-gap failures into SFT data pipelines; turn reasoning/composition failures into verifiable, exact-match RL tasks
• Run infrastructure at scale — build and maintain high-performance infrastructure against external, third-party LLMs (experimental, large-scale, and publicly deployed) so research and optimization loops run reliably without human intervention
• Keep it interpretable — every loop stays transparent code, explicit prompts, and clear diagnostics, never opaque parameter updates