Principal ML Engineer @ Rebar
AI Startup
New York, NYOn-siteFull-time$220k – $350k / year
About this role
Principal ML Engineer @ Rebar
!Rebar(https://app.ashbyhq.com/api/images/org-theme-wordmark/dfbed057-e8d6-4e63-abc6-3832cf9df8e1/1a1dc4f9-27fa-414e-8f91-29371207ade9/f1dcefae-3baa-44e2-a95c-159ba4bad4b5.png)(https://withrebar.ai)
(https://jobs.ashbyhq.com/rebar)
Principal ML Engineer
Location
New York City
Employment Type
Full time
Location Type
On-site
Department
Engineering
Background
Rebar is building the AI operating system for commercial HVAC, Electrical, and Plumbing.
Over the past year our quoting platform has processed tens of thousands of projects across North America and we’ve doubled our revenue in the first 6 weeks of this year. Our customers include many of the top firms in the industry. Some of these companies are running billion dollar construction projects on workflows that still look like it’s 1985.
Construction is 10% of GDP and still massively underserved by software. We are changing that.
We recently raised a $14M Series A from leading construction tech investors and are entering our next phase of growth. We are building a set of AI native products that will define how this industry operates.
We're looking for a Principal ML Engineer to help define the future of AI at Rebar. In this role, you'll combine hands-on technical excellence with long-term technical leadership, driving our strategy for computer vision systems, training infrastructure, and data. You'll work alongside a small, highly capable engineering team to turn cutting-edge research into reliable, production-ready AI systems that solve real problems for our customers.
This role is ideal for someone who enjoys staying deeply technical while shaping how AI is built across an organization. You'll lead by example through architecture, technical direction, mentorship, and execution rather than people management.
Responsibilities
Model Training & Development: Design and train deep learning models for layout analysis, image-to-graph, object detection,