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Member of Technical Staff - Infrastructure
San Francisco, USAPosted 2 months ago
Full-timeremote
Job Description
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industries, spending $7 trillion annually on labor, but productivity losses cost $1.6 trillion per year due to outdated management tools.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