Y
AI Engineer: Computer Vision, LLMs & ML
your Jared
1 hour ago
Full-time
On-site
San Francisco, California, United States
Work with ex-Narvar Founding CTO (Unicorn, $1B+). 6 AI patents. Enterprise AI pedigree: Google DCDE, Oracle, Macy's, Walmart Labs.
We're hiring founding team members to transform how the $12T global construction industry builds—from residential to mission-critical infrastructure.
Engineer the AI driving next-generation construction.
Location: Remote-US (San Francisco Bay Area Hybrid Preferred)
Type: Full-time
Compensation: Founding-team equity (1–2%) + base salary
Construction creates the world around us—yet still runs on WhatsApp, spreadsheets, and gut feel. Sites generate terabytes of data daily—yet none of it tells anyone what to do next.
This isn't a data problem. It's an Intelligence problem.
We're building the nervous system for construction. Think Palantir meets Procore, but actually usable by contractors. Our first customers are already begging for access.
You'll be the founding AI engineer, building alongside our founder and construction industry veterans. Async-first. Remote-friendly. Zero bureaucracy. No meetings about meetings. Just ship code that moves dirt and dollars.
You'll turn cutting-edge LLM and vision research into tools that run on dusty job sites and mobile devices.
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.
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
You are:
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
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.
Real-world impact: Your models will help prevent workplace injuries and save lives
Unique datasets: Access to proprietary construction data that competitors don't have
Greenfield opportunity: Define the AI strategy from day one
Domain expertise: Work directly with construction industry veterans
Mission-critical scale: Your models will power verification for facilities where downtime isn't an option
Pedigree: A Stanford StartX company (elite sub-1% accelerator)
You'll complete two quick assessments to show us what you can do.
Top scorers get interviewed. We move fast. No bureaucracy.
Intellus Build is an equal opportunity employer. We welcome candidates from all backgrounds.
#J-18808-Ljbffr
We're hiring founding team members to transform how the $12T global construction industry builds—from residential to mission-critical infrastructure.
Engineer the AI driving next-generation construction.
Location: Remote-US (San Francisco Bay Area Hybrid Preferred)
Type: Full-time
Compensation: Founding-team equity (1–2%) + base salary
Construction creates the world around us—yet still runs on WhatsApp, spreadsheets, and gut feel. Sites generate terabytes of data daily—yet none of it tells anyone what to do next.
This isn't a data problem. It's an Intelligence problem.
We're building the nervous system for construction. Think Palantir meets Procore, but actually usable by contractors. Our first customers are already begging for access.
You'll be the founding AI engineer, building alongside our founder and construction industry veterans. Async-first. Remote-friendly. Zero bureaucracy. No meetings about meetings. Just ship code that moves dirt and dollars.
You'll turn cutting-edge LLM and vision research into tools that run on dusty job sites and mobile devices.
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.
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
You are:
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
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.
Real-world impact: Your models will help prevent workplace injuries and save lives
Unique datasets: Access to proprietary construction data that competitors don't have
Greenfield opportunity: Define the AI strategy from day one
Domain expertise: Work directly with construction industry veterans
Mission-critical scale: Your models will power verification for facilities where downtime isn't an option
Pedigree: A Stanford StartX company (elite sub-1% accelerator)
You'll complete two quick assessments to show us what you can do.
Top scorers get interviewed. We move fast. No bureaucracy.
Intellus Build is an equal opportunity employer. We welcome candidates from all backgrounds.
#J-18808-Ljbffr