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# AI Engineer: Computer Vision, LLMs & ML

- **Company:** Intellus Build, Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Python (Programming Language), Machine Learning, Tensorflow, Unstructured Data, Data Processing, Pytorch, Large Language Models, Model Validation, Kaggle, Stack Overflow, Machine Learning Operations
- **Published:** August 9, 2026
- **Apply:** https://www.wayup.com/i-j-Founding-AI-Engineer-Applied-ML-Vision-LLM-Intellus-Build-541118232395323/

## About the Role

+ Recent graduate from top AI program (Stanford AI Lab, MIT CSAIL, or equivalent) OR 2-3+ years building production ML systems
 + Focused on practical AI applications, not just research demos
 + Comfortable with the full ML stack: data processing * model selection * deployment * monitoring
 + Able to move quickly-you prototype in hours, not weeks, + Shipped at least one LLM-based application used by real users
 + Experience with RAG, embeddings, and vector databases
 + Strong Python skills plus PyTorch, TensorFlow, or JAX
 + Ability to explain complex ML concepts to non-technical stakeholders, + Computer vision experience (YOLO, Segment Anything, etc.)
 + Published ML research or Kaggle competition medals
 + Experience with construction, manufacturing, or industrial datasets
 + Track record of optimizing inference costs

## Description

Intellus Build is the Infrastructure of Truth -the AI-native operating system that connects dirt to dollars.
The Problem: Construction sites generate terabytes of unstructured data daily-photos, documents, videos, sensor readings. Currently, this valuable information goes to waste.
Your Mission: Build AI systems that transform construction chaos into actionable intelligence.

What You'll Build
As the founding AI engineer, you'll tackle problems that don't have Stack Overflow answers:

 + Build RAG systems that understand construction terminology-teach AI the difference between 'pour concrete' and 'poor concrete'
 + Deploy computer vision that detects safety violations from grainy phone photos taken at 6 AM
 + Create AI assistants that answer 'What's the status of the Stanford dorm project?' by reasoning across blueprints, contracts, RFIs, and daily photo logs
 + Design real-time progress tracking that works even when construction sites have terrible WiFi
 + Build domain-aware AI that makes construction sites safer and more efficient
 + Build verification systems that track equipment from PO to energization across complex supply chains, We're flexible on the stack, but likely:
 + LLM APIs: OpenAI, Anthropic, Gemini-multi-model approach
 + Orchestration: LangChain, LlamaIndex, or custom frameworks
 + Vector stores: Pinecone, Weaviate, or pgvector
 + ML frameworks: PyTorch, TensorFlow, or JAX
You'll help shape these choices as we build.


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