Applied Machine Learning Engineer

PermitFlow

New York (NY)

Hybrid

USD 175,000 - 245,000

Full time

14 days+

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Benefits offered by this job

Competitive salary and equity

100% paid health, dental, and vision coverage

Company laptop and equipment stipend

Daily meals via UberEats

Unlimited PTO

Job summary

A construction technology company in New York is seeking an Applied Machine Learning Engineer to develop ML foundations for AI agents and manage the full ML lifecycle. Ideal candidates will have expertise in NLP and LLMs, strong Python and cloud infrastructure skills, and a passion for driving impactful AI solutions. This full-time role offers a competitive salary and flexible working arrangements.

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).
Strong track record of deploying and scaling ML systems with measurable business impact.

Responsibilities

Design, implement, and optimize LLM-powered models for document processing.
Develop retrieval-augmented generation (RAG) pipelines and search systems.
Own the full ML lifecycle: experimentation, deployment, monitoring.

Skills

Machine learning engineering

Natural Language Processing (NLP)

Python

Cloud ML infrastructure (AWS, GCP, or Azure)

System design and architecture

Tools

OpenAI GPT

FAISS

Elasticsearch

PyTorch

TensorFlow

Job description

Overview

Applied Machine Learning Engineer role at PermitFlow. This position focuses on developing ML foundations for PermitFlow’s AI agents and driving end-to-end ML lifecycle from research to production deployment.

Base pay range

$175,000.00/yr - $245,000.00/yr

About PermitFlow PermitFlow is redefining how America builds. Pre-construction remains one of the most broken and manual parts of the $1.6T construction industry, causing delays, wasted capital, and lost opportunity across the built world. Our AI workforce delivers speed, accuracy, and visibility to pre-construction — accelerating housing development, enabling clean-energy projects, and driving economic growth in communities nationwide. We’ve powered over $20B in real estate development, helping builders and contractors move faster, reduce risk, and scale with confidence. We are entering hypergrowth with clear product-market fit and a world-class team from top AI and construction companies. We’ve raised over $36.5M from investors including Kleiner Perkins, Initialized Capital, Y Combinator, Felicis Ventures, and Altos Ventures, alongside backers from 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.

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

Benefits