Applied ML Engineer: LLM/NLP for Autonomous Permitting

PermitFlow; United States; Hybrid; USD 175,000 - 250,000; Full time

Benefits offered by this job

Equity

Healthcare

401(k)

Unlimited PTO

Home office stipend

Meals provided

Commuter benefits

Job summary

PermitFlow in New York City offers a hybrid role for an Applied Machine Learning Engineer to build ML foundations for AI agents that process documents, extract insights, and power autonomous permitting workflows.

You will own the end-to-end ML lifecycle, from research and data engineering to production deployment and continuous evaluation, collaborating with engineering, product, and domain experts to deliver scalable ML-powered solutions.

Qualifications

• 5+ years of experience in machine learning engineering, with production ML experience.
• Deep expertise in NLP and LLMs (OpenAI GPT, Claude, Hugging Face models).
• Experience building retrieval and vector search systems (e.g., FAISS, Elasticsearch, Pinecone, Weaviate).
• Proficiency in Python and ML frameworks like PyTorch or TensorFlow.
• Strong track record of deploying and scaling ML systems with measurable business impact.
• Experience with cloud ML infrastructure (AWS, GCP, or Azure).
• Strong system design and architectural thinking, with a bias toward shipping and iterating quickly.

Responsibilities

• Design, implement, and optimize LLM-powered models for document processing, data extraction, and permit workflow automation.
• Develop retrieval-augmented generation (RAG) pipelines and search/retrieval systems for jurisdictional and regulatory data.
• Rapidly prototype, fine-tune, and evaluate pre-trained models for real-world NLP tasks like classification, entity recognition, and summarization.
• Build scalable ML infrastructure and backend services, integrating models into production systems that power AI agents.
• Work with large structured and unstructured datasets to improve indexing, retrieval, and contextual accuracy.
• Own the full ML lifecycle: experimentation, deployment, monitoring, evaluation, and iteration.
• Balance ML, retrieval, and rule-based approaches to ship reliable, maintainable, and high-impact AI features.
• Collaborate with engineering, product, and domain experts to shape ML-powered solutions for complex pre-construction challenges.

Skills

NLP/LLMs

Python

PyTorch/TensorFlow

ML deployment

Cloud ML infra

System design

Startup environment

Tools

FAISS

Elasticsearch

Pinecone

Weaviate

Job description

PermitFlow in New York City offers a hybrid role for an Applied Machine Learning Engineer to build ML foundations for AI agents that process documents, extract insights, and power autonomous permitting workflows.

You will own the end-to-end ML lifecycle, from research and data engineering to production deployment and continuous evaluation, collaborating with engineering, product, and domain experts to deliver scalable ML-powered solutions.