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Member of the Technical Staff

Poetiq · Los Altos

Extracted from the posting

Base
$150,000 – $500,000
Remote
Fully on-site in Los Altos, in-person required
Eng team
10 engineers and researchers
Stack
Python, LLM APIs, Kubernetes

Member of the Technical Staff role at Poetiq focused on the Recursive Self Improvement (RSI) loop and the systems that run it autonomously at scale. Work includes building RSI algorithms, reasoning strategies, dynamic task harnesses, and parallel orchestration over third-party LLMs. The stack centers on LLM orchestration, SFT data pipelines, and verifiable exact-match RL tasks.

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. Every MTS shares ownership of the same mission: advance the frontier of AI through 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.

Compensation: $150K - $500K

Technologies

PythonLLM APIsKubernetes
Poetiq
Poetiq
Website

Job details

TypeFull-time
Work ModeOn-site
LevelMid Level
Location🇺🇸Los Altos, CA
Salary$150,000 - $500,000
PostedNov 06, 2026
Apply Now

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