Senior Deep Learning Engineer

Rebar•New York City, NY

2d•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 Senior Deep Learning Engineer with extensive experience in modern neural network techniques and PyTorch to help us push the boundaries of computer vision in real-world environments. You’ll be joining a small, highly capable team focused on delivering practical, production-ready ML systems — from data pipelines through to fine-tuned models — in a fast-moving startup context. This role is ideal for someone who enjoys working with models under the hood, building and adapting training workflows, and applying research ideas to novel engineering challenges. Our work involves more than model inference — we design training workflows, develop evaluation pipelines, and engineer solutions that go beyond standard model usage.

Requirements

Master's degree or PhD in Computer Science, Electrical Engineering, or other relevant field with main focus on deep learning.
Proven ability to implement and adapt techniques or architectures from academic or industry literature.
Proven track record tackling novel ML challenges in the field of Deep Learning.
3+ years of experience developing and adapting model architectures with PyTorch.
2+ years of experience with deep learning for computer vision applications, especially semantic segmentation or object detection.
2+ years of experience with production-level code development and optimization.

Nice To Haves

Experience with active learning setups
Applied experience with RLHF (Reinforcement Learning from Human Feedback)
Published research developing SOTA computer vision or (or other DL) models
Experience with deployment and monitoring pipelines for ML systems.

Responsibilities

Design and train deep learning models for layout analysis, OCR, object detection, image to graph, and other related tasks. In some cases, you’ll extend or adapt existing architectures; in others, you’ll help design custom approaches from the ground up.
Build robust metrics, monitor production model performance, and proactively identify failure modes and areas for improvement.
Work closely with the engineering team to integrate models into our product and infrastructure. Participate in architecture and roadmap decisions.