Principal AI Engineer

We are seeking a

Research-Grade Engineer

(Masters/PhD preferred) who combines deep theoretical knowledge of NLP with the ability to architect scalable, production-ready systems.

You will design systems that can handle

massive context windows , maintain

semantic integrity

across thousands of files, and deliver

verifiable accuracy .

You will define the methodologies to constrain Generative AI with strict structural rules, answering the hard question: How do we build a system that possesses the flexibility of a neural network but the reliability of a compiler?

What You Will Do

Architecture Design:

Architect high-reliability inference systems that solve the "hallucination problem" inherent in Large Language Models. You will move beyond out-of-the-box solutions to build defensible, proprietary IP.

Advanced NLP Strategy:

Define the strategy for domain adaptation and long-context reasoning. You will perform first-principles analysis to select the right approach (RAG, Fine-Tuning, or novel methods) based on rigorous benchmarking.

Evaluation & Verification:

Design and build proprietary evaluation frameworks to rigorously measure the performance and safety of our models before they touch client code.

Technical Standards:

Mentor the engineering team on the mathematical underpinnings of Transformer architectures and current SOTA research.

What We Need

Advanced Degree:

Masters or PhD in Computer Science, AI, or related field (or equivalent top-tier research lab experience).

Advanced LLM Internals:

You understand the specific failure modes of modern architectures regarding

long-context recall ,

reasoning drift , and

hallucination triggers

in complex logic. You don't just fine-tune; you know how to mathematically constrain model outputs to ensure high-fidelity results.

Applied Research:

8+ years of experience, with a track record of taking complex ML research and deploying it into production environments.

Beyond APIs:

Experience building custom inference pipelines, optimizing vector search algorithms, or designing complex retrieval systems.

Engineering Excellence:

Strong proficiency in Python. You write clean, modular, object-oriented code, not just "notebook scripts."

Preferred Experience

Interest in

Code Generation ,

Program Analysis , or

Semantic Parsing .

Experience with open-source LLM orchestration (LangChain, DSPy, LlamaIndex) but with a critical understanding of their limitations.

Published research or technical blog posts on Applied NLP.

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