Member of the Technical Staff

Poetiq • Los Altos, CA

1d • $150,000 - $500,000 • Onsite

About The Position

Recursive Self Improvement is the most important frontier in AI research. We’re hiring a team of the best researchers and engineers dedicated to advancing RSI. If you’re driven to advance the frontiers of AI through RSI and optimization, come apply to be a Member of the Technical Staff at Poetiq. This is a single, flexible technical role that spans both the algorithmic core of our RSI loop and the high-performance systems that let it run autonomously at scale. Depending on your background, you'll advance deep algorithmic research, rigorous systems engineering, or a genuine hybrid of both. Every MTS shares ownership of the same mission: advance the frontier of AI through RSI.

Requirements

• Autonomy to turn both well-defined and highly ambiguous ideas into production-grade reality.
• First-principles understanding of LLM reasoning and failure modes, plus hands-on experience with LLM “quirks.”
• You want to build loops where the system learns from each problem it solves and gets better at the next one.
• Fully on-site in Los Altos — this is a high-bandwidth, whiteboard-heavy, low-ego, in-person team, and that's a fit requirement, not a preference.
• Commitment to code quality, rigorous testing, performance profiling, deliberate architectural design, and clear communication.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed