Applied Machine Learning Engineer

Job Description

Job Description

🚀 About PermitFlow

PermitFlow is building AI agents for the $1.6T construction industry. We’re creating the leading pre-construction platform, starting with the $12B permitting market.

Our platform automates the slow, manual permitting process for builders, covering everything from jurisdiction research to application preparation, submission, and real-time tracking. By transforming fragmented regulations and manual workflows into structured, intelligent systems, we help contractors move faster, reduce risk, and scale with confidence.

We've raised $36.5M+ with Kleiner Perkins leading our Series A, joined by Initialized Capital, Y Combinator, Felicis Ventures, and Altos Ventures. Our backers include founders and executives from OpenAI, Google, Procore, ServiceTitan, Zillow, PlanGrid, and Uber.

We are a team of architects, engineers, permitting experts, and product builders who have felt the pain of pre-construction firsthand and are committed to fixing it. Demand is growing faster than we can meet, and we’re hiring top talent to help us scale.

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.

✅ What You’ll Do

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.

You will :

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

Comfort operating in fast-moving startup environments with high ownership and autonomy

💙 Benefits

📈 Competitive salary and meaningful equity

🩺 100% paid health, dental, and vision coverage

💻 Company laptop and equipment stipend

🍽️ Daily meals via UberEats and a fully stocked kitchen

🚍 Commuter benefits

🎤 Team building events and offsites

🌴 Unlimited PTO

Compensation Range : $175K - $245K