Principal Deep Learning Engineer

About Rebar

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 hiring a Deep Learning Engineer with experience in modern neural network techniques and PyTorch to help push the boundaries of computer vision in real-world environments. You’ll join 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 environment.

This role is well suited for someone who enjoys working closely with models, building and adapting training workflows, and applying research ideas to novel engineering challenges. Our work goes beyond model inference — we design training workflows, develop evaluation pipelines, and build systems that extend standard model usage.

Responsibilities

• Model Training & Development – Design and train deep learning models for layout analysis, OCR, object detection, image-to-graph, and related tasks. This may include adapting existing architectures or contributing to new approaches where needed.
• Evaluation and Monitoring – Build metrics, monitor model performance in production, and help identify areas for improvement over time.
• Collaboration and Integration – Work closely with the engineering team to integrate models into product and infrastructure, and contribute to architectural and roadmap discussions.

What We’re Looking For

We’re looking for someone who is comfortable implementing training logic, experimenting with model internals, and debugging real-world issues that arise when bringing ML systems into production.

You may be a strong fit if you enjoy working across the full ML stack, going deep in PyTorch, and translating ideas into practical, production-ready systems.

Required Qualifications

• Master’s degree or PhD in Computer Science, Electrical Engineering, or a related field with a focus on deep learning
• Experience implementing or adapting techniques from academic or industry literature
• Demonstrated ability to work on challenging ML problems in deep learning
• 3+ years of experience developing or adapting model architectures with PyTorch
• 3+ years of experience applying deep learning to computer vision tasks such as segmentation or object detection
• Experience contributing to production-level code and system optimization

Nice to Have

• Experience with active learning setups
• Applied experience with RLHF (Reinforcement Learning from Human Feedback)
• Published research in computer vision or deep learning
• Experience with deployment and monitoring pipelines for ML systems

Compensation and Benefits

• Salary: $200K–$350K, depending on experience
• Equity: Meaningful equity package
• Benefits: Medical, dental, and vision coverage
• Perks: Free lunches and dinners

This is a full-time, onsite role based in New York City. Being onsite enables close collaboration, faster iteration, and strong team connection as we continue to build and grow.