Head of AI

Rebar•New York, NY

1d•Onsite

About The Position

Rebar is building the next-generation operating system for commercial HVAC, electrical, and plumbing suppliers and subcontractors. Over the past year, our V1 quoting product has scaled to thousands of quotes completed weekly, doubled revenue in 2026, and gained adoption across many of the top suppliers in North America. Fresh off a $14M Series A backed by leading construction tech investors, we're entering our next phase of growth — with AI at the center of everything we build next. We’re looking for a Head of AI to lead the next chapter of our machine learning efforts. This is a highly technical, hands-on leadership role for someone who has deep expertise in modern deep learning systems and wants to shape both the strategy and execution of AI at a fast-moving startup. You’ll own the direction of our AI stack across model development, training workflows, evaluation systems, and production performance. You’ll also serve as a senior technical resource for the team — helping guide architecture, mentor engineers, and push forward novel approaches to hard computer vision and document understanding problems in real-world environments. This role is ideal for someone who still loves being close to the work, but is excited to operate at a higher level as well: setting technical direction, building systems that scale, and helping a strong team level up around them.

Requirements

Deeply fluent in modern deep learning and comfortable operating across the full ML lifecycle — from architecture decisions and training code to evaluation design and production iteration.
Ability to both go deep technically and lead from the front.
Experience building real systems.
Strong judgment on what works in practice.
Ability to help a startup navigate ambiguous, open-ended modeling problems with speed and rigor.
Master’s degree or PhD in Computer Science, Electrical Engineering, or another relevant field, with a strong focus on deep learning
6+ years of experience working on deep learning systems, with significant experience in computer vision
4+ years of experience building and adapting model architectures in PyTorch
3+ years of experience applying deep learning to computer vision problems such as object detection, semantic segmentation, OCR, document understanding, or related areas
Proven ability to implement, adapt, and improve techniques from academic or industry literature
Proven track record of solving novel ML problems and shipping production-ready systems
Experience owning or leading technical direction for complex ML initiatives
Strong experience writing production-grade code and building reliable, scalable ML workflows

Nice To Haves

Experience leading or mentoring ML engineers in a startup or high-ownership environment
Experience with active learning systems
Experience with RLHF or other human-in-the-loop training paradigms
Published research in deep learning, computer vision, or related fields
Experience building deployment, evaluation, and monitoring pipelines for ML systems
Experience working on document AI, layout analysis, multimodal systems, or image-to-structured-data problems

Responsibilities

Own AI Strategy and Technical Direction: Set the direction for AI development at Rebar across computer vision, document understanding, OCR, layout analysis, image-to-graph, and adjacent modeling problems.
Lead Model Development: Architect, train, and improve deep learning systems for production use cases. In some cases this means extending state-of-the-art approaches; in others, designing custom solutions from the ground up.
Build and Oversee ML Systems: Drive the development of training workflows, evaluation frameworks, monitoring systems, and feedback loops that make our models more accurate, reliable, and scalable over time.
Mentor and Uplevel the Team: Act as a senior technical resource for engineers working on ML problems. Provide guidance on modeling decisions, experimentation, implementation details, and best practices across the stack.
Partner Cross-Functionally: Work closely with engineering, product, and leadership to identify the highest-leverage AI opportunities, make smart tradeoffs, and translate technical work into product impact.
Own Production Performance: Establish strong metrics, proactively identify failure modes, and lead the team in improving model quality in real-world deployments.