About Ironsite

Construction is one of the most complex and labor-intensive industries, spending $7 trillion annually on labor, but productivity losses cost $1.6 trillion per year due to outdated management tools.

Ironsite leverages wearable cameras combined with human labeling and AI vision language models to drive on-site productivity, safety & training for crafts workers. We put cameras on construction workers' hard hats and vests to analyze what's actually happening on job sites.

We help teams cut labor costs, improve safety, and finish projects faster. We have captured ~50,000 hours of construction footage across 7 states and just partnered with the nation's #2 hard hat manufacturer. We’ve raised over $13M in seed funding from 8VC, South Park Commons, and 30+ leading operators and technologists, including Eric Glyman (Ramp), Jeff Dean (Google), and Scott Wu (Cognition).

The Role

We're seeking an exceptional Infrastructure Engineer to build the backbone that connects ruggedized wearable devices on 11 million construction workers to the AI systems making sense of what they capture. You'll work directly with our folks across our engineering org to own the pipelines that ingest massive amounts of egocentric video data and the systems that power VLM inference on neural accelerator chips. From device-to-cloud ingestion to foundation model training at scale, you'll bridge the gap between job site hardware and cutting-edge AI—across some of the harshest environments in American industry.

We're scaling toward 2.5 million hours of job site footage in 2026. We've assembled a team that's never existed before in construction—engineers from DeepMind, YouTube, Etched, Meta, Apple and NVIDIA working alongside construction veterans from Turner, DPR, and Cahill Construction.

What You'll Build

Scale inference and training pipelines to support foundation model development on 15M+ hours of egocentric construction video
Build custom data pipelines to manage and process first-person job site footage for our CV and VLM teams
Design device-to-cloud ingestion systems for seamless, reliable data transfer from our wearable devices after every shift
Deploy and optimize VLM inference on neural accelerator chips across many environments (edge, on premises, cloud, etc)
Improve and scale our internal research platform and multi-environment compute orchestrator
Solve video storage and distribution challenges at YouTube-scale across high-resolution footage
Support automated reporting infrastructure delivering AI-powered productivity insights to field leaders by 5:00 AM daily
Build privacy and compliance controls including automated face and phone blurring to protect workers on-site
Ensure edge reliability on IP67-rated devices operating in harsh construction environments

What We're Looking For

Required Qualifications

Bachelor's degree in Computer Science, Electrical Engineering, or related field
5+ years of experience in infrastructure, ML systems, or platform engineering
Proficiency in cloud infrastructure and distributed systems, with a focus on IoT or embedded devices

Preferred Qualifications

Experience architecting and maintaining large-scale data or training pipelines
Familiarity with edge ML inference and neural accelerator chips
Experience with IoT networking and traditional multi-environment HPC networking
Proven ability to own the full lifecycle of systems across hardware, firmware, and software integration
High surface-area observability across disciplines
Multiple security facets for securing edge devices and video data across a large lifecycle

Location & Compensation

San Francisco, CA (on-site)
Hyper competitive salary and equity package
Ability to purchase xPU or devices for development
Full benefits including health, dental, vision, and 401k +6% match

Compensation

The base pay range for this role is $175,000 – $325,000 per year.

Member of Technical Staff - Infrastructure