Applied Machine Learning Engineer
Role summary
PermitFlow is seeking an Applied Machine Learning Engineer to build the ML foundation for their AI agents. This role involves designing, prototyping, and deploying intelligent systems for document processing, insight extraction, and autonomous permitting workflows. You will manage the full ML lifecycle, from research and data engineering to production deployment and evaluation, with a focus on LLM-powered models, RAG pipelines, and NLP tasks. The engineer will also build scalable ML infrastructure and backend services, work with large datasets, and balance ML, retrieval, and rule-based approaches. Collaboration with cross-functional teams is key to shaping ML-powered solutions for complex pre-construction challenges. Requires 5+ years of experience in ML engineering with production ML experience, proficiency in Python, ML frameworks (PyTorch/TensorFlow), cloud ML infrastructure (AWS/GCP/Azure), retrieval systems, NLP, LLMs, and system design.
- 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
- 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
### Benefits
- Equity packages
- 100% Paid health, dental & vision coverage
- Company issued laptop
- Home office & equipment stipend
- Lunch & Dinner provides via UberEats w/ a fully stocked kitchen
- Commuter benefits
- Team building events
- Unlimited PTO- Strong system design and architectural thinking, with a bias toward shipping and iterating quickly
- Experience with cloud ML infrastructure (AWS, GCP, or Azure)
- Experience building retrieval and vector search systems (e.g., FAISS, Elasticsearch, Pinecone, Weaviate)
- Deep expertise in NLP and LLMs (OpenAI GPT, Claude, Hugging Face models)
- Proficiency in Python and ML frameworks like PyTorch or TensorFlow
- Strong track record of deploying and scaling ML systems with measurable business impact
- Comfort operating in fast-moving startup environments with high ownership and autonomy
- 5+ years of experience in machine learning engineering, with production ML experience
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