---
title: 'AI Engineer at Poetiq'
canonical: 'https://feeny.ai/job/ai-engineer-poetiq-los-altos-cr31tpzzjk3s'
type: 'job'
last_seen: '2026-09-11'
---

# AI Engineer at Poetiq

- **Company:** Poetiq
- **Location:** Los Altos, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-06
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.ashbyhq.com/poetiq/1ca703c8-394d-4b6d-a907-50096b1cc176

## Job description

The Vision: System-Level Optimization Most current RSI work is highly LLM-centric, treating model weights as the sole unit of improvement. Every step inherits the massive cost of a training run, and compounding progress arrives late. We take a whole-system view: the LLM is just one component of a larger reasoning system that includes code, prompts, search strategies, and tool use. We are building a self-optimizing optimizer—a system where every task it tackles supplies the signal needed to optimize its own orchestration code. As an AI Engineer, you will build a robust, scalable platform for our intelligence on top of foundation models. You will architect a complete system that surrounds the LLMs, managing the complex, multi-step interactions required to extract and synthesize information. You must be comfortable working in, and building, the high-performance infrastructure that makes our fast, self-improving reasoning possible.

You are a good fit if you:

- Think and breathe Python. You are a Python engineer proficient in building complex, agentic systems or multi-step, stateful execution frameworks.
- Work autonomously to develop both clearly defined and ambiguous ideas, including your own, into reality.
- Excel at designing and building reliable, high-performance infrastructure that interacts heavily with external, third-party LLMs – some experimental, some large-scale and publicly deployed.
- Can architect clean abstractions for complex workflows, specifically synthesizing fragmented information gathered over thousands of parallel, asynchronous queries.
- Care deeply about code quality, performance profiling, and building the stable, scalable platform that allows research to run autonomously.

Our Results & Impact Our system-level RSI reaches state-of-the-art (SOTA) performance without modifying a single LLM parameter:

- 12.3% boost on frontier models for LiveCodeBench Pro, setting a new SOTA at 93.9%, among many other SOTA results we have shared on our blog at [poetiq.ai](http://poetiq.ai).
- Universal improvement: Every model tested improved, proving our harness encodes highly transferable task structure.
- Dominated 6 unseen benchmarks spanning competition mathematics, scientific coding, long-horizon planning, agentic tool use, and long-context retrieval—all automatically.

Culture of Transparent, Inspectable AI We prioritize explainability. By running our optimization loops at the system level rather than baking them into uninterpretable parameters, every improvement remains human-readable—transparent code, explicit prompts, and clear data. We believe fast, powerful RSI is fully compatible with tighter, more deliberate oversight. This approach values thoughtful, rigorous diagnostic engineering over blindly scaling compute and black-box models.

## 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.

## About Poetiq

## Company Overview
- **One-liner**: Poetiq builds a self-improving reasoning system that sits on top of any frontier large language model, enabling automated recursive self-improvement for complex problem-solving.
- **Entity Type**: Private (Seed stage – $45.8M raised)
- **Headquarters**: Mountain View, California, United States
- **Founded**: 2025
- **Founders**: Not publicly disclosed (founding team includes veterans from Google and DeepMind)

## Core Business
- **Primary industry/industries**: Artificial Intelligence, AI Infrastructure, Machine Learning
- **Target customers**: B2B – enterprises and organizations that need reliable, high-performance reasoning for complex workflows (e.g., code generation, scientific research, data synthesis)
- **Mission or purpose statement**: “The fastest path to safe superintelligence. Paved with better reasoning.” [poetiq.ai](https://poetiq.ai/)

## Products & Services
- **[Meta-System]**: A proprietary reasoning engine that wraps around any LLM (ChatGPT, Claude, Gemini, etc.) to probe, extract, and synthesize fragmented knowledge. It uses recursive self-improvement loops to learn task-specific reasoning strategies with very little data (hundreds of data points instead of millions), enabling dramatic performance gains without retraining the underlying model. It has achieved state-of-the-art results on benchmarks such as ARC-AGI, LiveCodeBench, and Humanity’s Last Exam. [poetiq.ai](https://poetiq.ai/)

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding – $45.8M in a single Seed round (closed in late 2025 / early 2026)
- **Notable Investors/Partners**: Surface Ventures, FYRFLY Venture Partners, Y Combinator, 468 Capital, Operator Collective, NeuronVC, and HICO. [poetiq.ai/posts/seed_funding/](https://poetiq.ai/posts/seed_funding/)
- **Growth Signals**: 
  - Shattered the ARC-AGI-2 state-of-the-art with 54% accuracy (first to break 50%) at a cost of $30.57 per problem. [linkedin.com/company/poetiq](https://www.linkedin.com/company/poetiq)
  - Achieved SOTA on Humanity’s Last Exam and SimpleQA.
  - 7 employees with a monthly headcount growth of +22.2%; LinkedIn followers grew 4.2% monthly to ~2,400. [builtin.com/company/poetiq](https://builtin.com/company/poetiq)
  - Proven that the system can lift performance of smaller, cost-efficient models to compete with frontier reasoning models (e.g., matching Gemini Deep with a Flash model). [linkedin.com/company/poetiq](https://www.linkedin.com/company/poetiq)

## Competitive Advantages
- **Data‑efficient self‑improvement**: Poetiq’s method requires only hundreds of data points rather than the millions needed for reinforcement‑learning‑based post‑training, making it practical for most real‑world domains.
- **Model‑agnostic architecture**: The system sits on top of any LLM and adapts to each model’s quirks, meaning customers are not locked into a single model provider.
- **Continually improving**: Each solved problem automatically improves the system’s reasoning strategies, creating a compounding flywheel effect.
- **Team expertise**: The founding team has 72 years of combined experience at Google and DeepMind, giving them deep first‑principles understanding of LLMs and their failure modes. [poetiq.ai](https://poetiq.ai/)

## Strategic Focus
- **Scale the team and infrastructure**: With the fresh seed funding, Poetiq is aggressively hiring AI Scientists and AI Engineers to accelerate research and build a robust platform for commercial deployment. [poetiq.ai/careers/](https://poetiq.ai/careers/)
- **Commercialization**: Moving from research breakthroughs to practical, reliable reasoning systems that solve business workflows. “With this capital, we are accelerating the development of our core technology—a system that automatically creates expert agents capable of dramatically outperforming their underlying language models.” [poetiq.ai/posts/seed_funding/](https://poetiq.ai/posts/seed_funding/)
- **Open‑access vision**: “In a few months, we will be offering it for free” – suggesting a freemium or democratized access model to drive adoption. [poetiq.ai](https://poetiq.ai/)

## Why Work Here
- **Culture**: A small, fast‑moving team of top‑tier researchers and engineers (currently ~7 people) with a high‑trust, high‑autonomy environment. The careers page emphasizes “insanely passionate scientists and engineers” and “crazy ideas.” [poetiq.ai/careers/](https://poetiq.ai/careers/)
- **Work policy**: On‑site in Mountain View, California. [builtin.com/company/poetiq](https://builtin.com/company/poetiq)
- **Engineering culture**: Python‑heavy; focus on building complex agentic systems, multi‑step execution frameworks, and high‑performance infrastructure that interacts with external LLMs. Engineers are expected to work autonomously and turn ambiguous ideas into reality.
- **Impact**: Opportunity to contribute directly to the core algorithms and infrastructure of what the company calls “the fastest path to superintelligence.” The team is small enough that every hire has outsized influence on the product and research direction.

## Sources
1. [poetiq.ai](https://poetiq.ai/)
2. [poetiq.ai/careers/](https://poetiq.ai/careers/)
3. [builtin.com/company/poetiq](https://builtin.com/company/poetiq)
4. [linkedin.com/company/poetiq](https://www.linkedin.com/company/poetiq)
5. [poetiq.ai/posts/seed_funding/](https://poetiq.ai/posts/seed_funding/)

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