Applied Machine Learning Engineer

PermitFlow is redefining how America builds. We’re an applied AI company serving the nation’s builders, tackling one of the largest information challenges in the economy: understanding what can be built, where, and how . Our AI agent workforce helps the fastest-growing construction companies navigate everything from permitting and licensing to inspections and project closeouts – accelerating housing, clean-energy, and infrastructure development across the country.

Despite being a $1.6T industry, construction still suffers from massive delays, wasted capital, and lost opportunity. PermitFlow has already delivered unprecedented speed, accuracy, and visibility to over $20B in development, helping contractors reduce compliance time, de-risk projects, and scale with confidence.

As the U.S. enters a new capex supercycle across data centers, factories, housing, and renewables, joining PermitFlow means building the AI infrastructure at the core of every construction project driving the next wave of reindustrialization.

We’ve raised over $90M, most recently completing our Series B, from top-tier investors including Accel, Kleiner Perkins , Initialized, Y Combinator, Felicis, and Altos Ventures, with backing from leaders at OpenAI, Google, Procore, ServiceTitan, Zillow, PlanGrid, and Uber.

Our HQ is in New York City with a hybrid schedule (3 in-office days per week). Preference for NYC-based candidates or those open to relocation.

Role Overview

As an Applied Machine Learning Engineer, you will develop the ML foundation for PermitFlow’s AI agents. You’ll design, prototype, and deploy intelligent systems that process documents, extract insights, and power autonomous permitting workflows. You will own the end-to-end ML lifecycle, from model research and data engineering to production deployment and continuous evaluation.

What You'll Do

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

What We’re Looking For

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