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?

• 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.

• 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."

• Interest in Code Generation
  , Program Analysis
  , or Semantic Parsing
  .
• Experience with open-source LLM orchestration (Lang Chain, DSPy, Llama Index) but with a critical understanding of their limitations.
• Published research or technical blog posts on Applied NLP.