Principal AI Engineer

Rebar•New York, NY

21h•Onsite

About The Position

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 AI 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.

Requirements

Master's degree or PhD in Computer Science, Electrical Engineering, or another relevant field with a strong focus on deep learning. Specialization in computer vision is a big plus.
Proven ability to implement, adapt, and extend techniques or architectures from academic and industry literature.
Proven track record solving novel deep learning problems and bringing research ideas into production.
4+ years developing and adapting deep learning models using PyTorch or JAX.
3+ years applying deep learning to computer vision problems such as object detection, semantic segmentation, OCR, or document understanding.
Experience building production-grade ML systems, including training pipelines, evaluation frameworks, and model deployment.
Experience defining evaluation methodologies and data strategies that improve model performance over time.
Experience designing active learning and continuous learning systems that keep models improving in production.
Demonstrated ability to lead technically complex initiatives and influence engineering decisions across teams.

Nice To Haves

Experience with synthetic data generation.
Experience with post-training of LLMs or VLMs — supervised fine-tuning (SFT), RLHF, and RLVR.
Experience optimizing and serving models in production — e.g. ONNX, quantization, and GPU kernels (CUDA/Triton).
Published research developing state-of-the-art deep learning models.
Experience building ML platforms or training infrastructure.
Experience mentoring engineers and establishing ML engineering best practices.
Experience deploying and monitoring production ML systems at scale.

Responsibilities

Design and train deep learning models for layout analysis, image-to-graph, object detection, OCR, and multimodal image–text understanding, among other related tasks. Extend existing architectures where appropriate and develop novel approaches when existing methods fall short.
Define the long-term technical direction for Rebar's AI capabilities. Evaluate emerging research, identify high-impact opportunities, and help shape the roadmap for our machine learning platform and modeling efforts.
Drive the strategy behind dataset creation, labeling, active learning, synthetic data generation, and evaluation. Build systems that continuously improve model quality and create durable competitive advantages through data.
Design robust evaluation methodologies, establish meaningful performance metrics, monitor production models, and proactively identify failure modes, regressions, and opportunities for improvement.
Serve as the technical lead for complex ML initiatives. Mentor engineers, review modeling approaches, establish engineering best practices, and raise the technical bar across the organization through design reviews and technical guidance.
Partner closely with engineering and product leadership to integrate AI capabilities into our platform, balancing research ambition with product impact. Influence architecture decisions and help shape the long-term AI roadmap.