🚀 About PermitFlow
PermitFlow’s mission is to streamline and simplify construction permitting in the $1.6 trillion United States construction market. Our software reduces time to permit, supporting permitting end-to-end including permit research, application preparation, submission, and monitoring.
We’ve raised a $31m Series A led by Kleiner Perkins with participation from Initialized Capital, Y Combinator, Felicis Ventures, Altos Ventures, and the founders and executives from Zillow, PlanGrid, Thumbtack, Bluebeam, Uber, Procore, and more.
Our team consists of architects, structural engineers, permitting experts, and workflow software specialists, all who have personally experienced the pain of permitting.
📌About the Team
We have a lean but mighty engineering team. We’ve done a lot with a little, but there’s much more work to be done to continue our fast-paced growth and we want you to be a part of that growth. You’ll help us get there by owning end-to-end projects, talking with customers, and ultimately supporting the growth of PermitFlow.
Ideally, you’re based in NYC or willing to relocate for hybrid work. We’re currently hybrid in-person 3 days/week in our NYC office.
✅ What You’ll Do:
We are seeking a Machine Learning Software Engineer to lead the development of intelligent systems that enhance our core permitting workflows — from document understanding and data extraction to compliance automation. You’ll be instrumental in designing and scaling LLM-powered features and retrieval systems to extract, analyze, and structure complex permitting data. This is a high-impact, high-autonomy role where you’ll help define the technical foundation of our ML stack and influence product direction from day one.
Design, implement, and optimize LLM-powered models for document processing, data extraction, and permit application workflows.
Develop and optimize retrieval-augmented generation (RAG) pipelines to improve query processing and information retrieval.
Experiment with pre-trained models and fine-tune them for permit-related NLP tasks, such as document classification and entity recognition.
Build scalable machine learning infrastructure, integrating with backend systems to support AI-driven workflows.
Work with large-scale structured and unstructured data to ensure efficient indexing, retrieval, and contextual relevance.
Own the full ML lifecycle — from experimentation and evaluation to deployment, monitoring, and continuous improvement.
Stay on the frontier of research in LLMs, NLP, and information retrieval — applying best practices and new techniques to production use cases.
Collaborate cross-functionally with engineering, product, and domain experts to shape AI-native solutions for complex regulatory challenges.
🙌 Qualifications & Fit:
5+ years of experience in machine learning engineering, with a strong track record of deploying and scaling models in production environments.
Deep expertise in natural language processing and large language models (e.g., OpenAI GPT, Claude, Hugging Face models).
Practical experience with retrieval systems (e.g., FAISS, Elasticsearch, vector databases).
Proficient in Python and ML frameworks like PyTorch, TensorFlow, and ecosystem tools like scikit-learn.
Experience with ML model deployment, monitoring, and scaling in a cloud environment (AWS, GCP, or Azure).
Strong architectural thinking, ability to reason across the full ML stack, and a bias for shipping fast and iterating.
Comfortable working in fast-moving, ambiguous startup environments with a high degree of ownership and autonomy.
💙 Benefits
📈 Equity packages
💰 Competitive Salary
🩺 100% Paid health, dental & vision coverage
💻 Home office & equipment stipend
🍽️ Lunch & Dinner provided via UberEats w/ a fully stocked kitchen
🚍 Commuter benefits
🎤 Team building events
🌴 Unlimited PTO