Zero RFI is an AI-native platform for the construction industry that integrates AI across tech-enabled service firms to eliminate RFIs and improve project coordination.
We are seeking a Senior ML Engineer to own the machine learning function at Zero RFI — building, deploying, and continuously improving the models that power our construction intelligence platform. You will architect production ML systems, lead a growing team of engineers, and work directly at the intersection of deep learning, structured construction data, and the real-world workflows of owners, contractors, and project teams. This is not a research role. You will ship models into production, measure their impact on active construction programs, and iterate fast. You will also be a technical lead — mentoring engineers, setting ML standards, and collaborating with our Principal Engineer on system architecture and platform integration. This is a rare opportunity to apply state-of-the-art ML to one of the world's most data-rich and underserved industries. KEY RESPONSIBILITIES ML Engineering & Production Systems - Design, build, and deploy end-to-end ML pipelines — from data ingestion and feature engineering through model training, evaluation, and production serving — for AEC-specific use cases including document intelligence, schedule analytics, and cost prediction. - Architect scalable ML infrastructure using modern MLOps practices: experiment tracking (Weights & Biases, MLflow), model versioning, A/B testing frameworks, and automated retraining pipelines. - Build and maintain NLP/LLM pipelines for AEC document processing — RFI parsing and response generation, submittal log classification, contract risk extraction, and change order analysis. - Develop computer vision systems for construction drawing analysis, defect detection from site photography, and progress monitoring from reality capture data. - Deploy physics-informed models and time-series forecasting systems for project schedule prediction, cost escalation detection, and construction performance analytics. - Implement graph neural networks and geometric deep learning models for BIM/IFC data analysis, spatial coordination, and MEP system optimization. - Integrate ML models with industry-standard tools (Revit, Procore, Autodesk Construction Cloud) through custom APIs and data connectors, ensuring models consume and produce data in the formats construction teams actually use. Technical Ownership - Define ML engineering standards: model evaluation frameworks, data versioning practices, testing strategies, and documentation requirements. - Drive ML strategy by evaluating emerging techniques and architectures — determining what to build, what to fine-tune, and what to buy — in collaboration with the CTO and Principal Engineer. - Collaborate with AEC domain experts (project managers, owners' reps, estimators) to translate field problems into well-scoped ML problems and validate outputs against ground truth. - Lead research initiatives where relevant and represent Zero RFI's technical perspective externally to establish credibility in the AEC ML space. Platform Integration - Partner with the Principal Engineer to integrate ML capabilities into core platform services — ensuring models operate as first-class components of the broader system architecture. - Own the ML layer of the data platform: feature stores, embedding infrastructure, vector search, and structured data pipelines that serve both real-time inference and batch analytics. - Champion responsible ML practices: bias evaluation, model transparency, and clear documentation of model limitations for non-technical stakeholders. REQUIREMENTS - Bachelor's or Master's degree in Computer Science, AI/ML, Statistics, Computational Engineering, or a related field (or equivalent practical experience). - 5–8 years of hands-on experience building and deploying ML models in production environments, with at least 2 years in a technical lead or senior individual contributor role. - Deep expertise with modern deep learning frameworks — PyTorch preferred — and strong proficiency in Python and scientific computing libraries (NumPy, SciPy, scikit-learn, Pandas). - Proven track record designing and shipping production ML pipelines in cloud environments (AWS SageMaker, Vertex AI, or Azure ML) with robust monitoring and retraining infrastructure. - Experience with NLP and LLM systems — fine-tuning, RAG architectures, prompt engineering at scale, and embedding-based retrieval (vector databases: Pinecone, Weaviate, Turbopuffer, or equivalent). - Strong foundation in computer vision — object detection, segmentation, or document understanding — using modern frameworks (YOLO, SAM, LayoutLM, or equivalent). - Experience with MLOps tooling: experiment tracking (W&B, MLflow), CI/CD for ML, containerization (Docker), and orchestration (Kubernetes or ECS). - Solid software engineering practices — clean code, code review, testing, version control — and the ability to collaborate fluidly with platform engineers. - Excellent communication skills: ability to explain model behavior, limitations, and tradeoffs to both technical teams and non-technical AEC stakeholders. PREFERRED QUALIFICATIONS - Experience with AEC data types: BIM/IFC schemas, construction schedules (P6, MS Project), RFI/submittal logs, cost databases, or CAD/drawing formats (DWG, PDF). - Familiarity with computational geometry, 3D scene understanding, or spatial data processing (Open3D, trimesh, PointNet++, or similar). - Experience with graph neural networks (PyTorch Geometric, DGL) for structured relational data — particularly useful for BIM element graphs and project dependency networks. - Background in time-series modeling for forecasting and anomaly detection in project performance data (schedule variance, cost burn, productivity metrics). - Knowledge of generative AI architectures (diffusion models, transformers, VAEs, GANs) and experience applying them to structured or domain-specific generation tasks. - Experience with reinforcement learning or multi-objective optim