Member of the Technical Staff

Poetiq Inc.; Los Altos (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Poetiq Inc. in Los Altos, CA, is seeking talented researchers and engineers to advance Recursive Self Improvement by designing core algorithms and scalable infrastructure for ongoing self-improvement of AI systems.

You will work on reasoning strategies, dynamic harnesses, and interpretable pipelines, collaborating in an in-office team to push capabilities while ensuring transparent prompts and diagnostics.

Qualifications

• Strong fundamentals in AI systems, ML, and reasoning.
• Hands-on experience with LLM quirks, failure modes, and diagnostic pipelines.
• Willingness to work on-site in Los Altos, CA with a high-bandwidth team.

Responsibilities

• Improve the RSI loop by designing and building core algorithms for system-wide self-improvement.
• Push reasoning strategy by testing novel approaches and surfacing latent knowledge.
• Build dynamic harnesses to create task-specific environments on the fly.

Skills

LLM reasoning

System design

Python/ML

Performance profiling

Tools

Python

PyTorch

Job description

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.

What You’ll Do

Your day-to-day depends on your focus, but the shared surface area includes:

• 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 You’ll Need

Everyone on technical staff:

• 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.

Our Team & Engineering Culture

We are a high‑leverage team of 10 engineers and researchers. We thrive in an in‑office environment built around high‑bandwidth collaboration, rapid whiteboarding, and low‑ego problem solving.

Our engineering culture is highly collaborative, mentorship‑driven, and deeply inclusive. We value clear communication, rigorous testing, and deliberate architectural design. At Poetiq, you won’t just be optimizing weights on the periphery; you will be core to designing the interpretable reasoning architectures of the future.