Rebar
Principal ML Engineer
New York City · Principal
Sponsorship not specifiedDetected 19 days ago
Machine LearningDeep LearningPyTorchComputer VisionLLMsMLOpsElectrical EngineeringResearchLeadershipCollaborationProblem SolvingMentoring
About the role
- 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 recently raised a $14M Series A from leading construction tech investors and are entering our next phase of growth.
Responsibilities
- Evaluation and Monitoring: Design robust evaluation methodologies, establish meaningful performance metrics, monitor production models, and proactively identify failure modes, regressions, and opportunities for improvement.
- Technical Leadership: 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.
- Collaboration and Integration: 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.
- Extend existing architectures where appropriate and develop novel approaches when existing methods fall short.
- 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.
- Rebar is building the AI operating system for commercial HVAC, Electrical, and Plumbing.
- We are building a set of AI native products that will define how this industry operates.
Requirements
- Experience defining evaluation methodologies and data strategies that improve model performance over time.
- You enjoy staying close to the code while thinking strategically about data, modeling, infrastructure, and the long-term evolution of AI capabilities.
Nice to have
- 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).
- Experience mentoring engineers and establishing ML engineering best practices.
- Experience deploying and monitoring production ML systems at scale.
- You'll be at the heart of our fast-paced operations, actively contributing to a culture that values engagement, growth, and teamwork.
Benefits
- Comprehensive medical, dental, and vision coverage
- 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.
- Meaningful equity package, commensurate with experience
- Model Training & Development: Design and train deep learning models for layout analysis, image-to-graph, object detection, OCR, and multimodal image-text understanding, among other related tasks.
Company info
- Our customers include many of the top firms in the industry.
- We are changing that.
- We're looking for a Principal ML Engineer to help define the future of AI at Rebar.
- 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 listing is sourced directly from Rebar's careers page and normalized into a canonical job model.