Lead ML Engineer

Key Responsibilities

• Design, build, and deploy end-to-end ML pipelines (data ingestion to production serving) for AEC-specific use cases: document intelligence, schedule analytics, cost prediction.
• Arcitect scalable ML infrastructure with MLOps practices (experiment tracking, model versioning, A/B testing, automated retraining).
• Build and maintain NLP/LLM pipelines for AEC document processing (RFI parsing, submittal classification, contract risk extraction, change order analysis).
• Develop computer vision systems for drawing analysis, defect detection, and progress monitoring.
• Deploy physics-informed models and time-series forecasting for schedule prediction, cost escalation, and performance analytics.
• Implement graph neural networks and geometric deep learning for BIM/IFC data analysis and MEP system optimization.
• Integrate ML models with industry tools (Revit, Procore, Autodesk Construction Cloud) via custom APIs and data connectors.

Technical Ownership

• Define ML engineering standards: evaluation frameworks, data versioning, testing strategies, documentation.
• Drive ML strategy (build vs. fine-tune vs. buy) in collaboration with CTO and Principal Engineer.
• Collaborate with AEC domain experts to translate field problems into ML problems and validate outputs.
• Lead research initiatives and represent Zero RFI's technical perspective externally.

Platform Integration

• Partner with Principal Engineer to integrate ML into core platform services.
• Own the ML layer of the data platform: feature stores, embedding infrastructure, vector search, structured data pipelines.
• Champion responsible ML practices: bias evaluation, model transparency, documentation of limitations.

Requirements

• Bachelor's or Master's in CS, AI/ML, Statistics, Computational Engineering, or equivalent.
• 5–8 years hands-on production ML experience, with 2+ years as technical lead or senior IC.
• Deep expertise with modern deep learning frameworks (PyTorch preferred), 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, Azure ML).
• Experience with NLP/LLM systems (fine-tuning, RAG, prompt engineering, embedding-based retrieval with vector databases).
• Strong foundation in computer vision (object detection, segmentation, document understanding) using modern frameworks.
• Experience with MLOps tooling (W&B, MLflow, CI/CD for ML, Docker, Kubernetes or ECS).
• Solid software engineering practices (clean code, code review, testing, version control).
• Excellent communication skills for technical and non-technical stakeholders.

Preferred Qualifications

• Experience with AEC data types: BIM/IFC, construction schedules, RFI/submittal logs, cost databases, CAD/drawing formats.
• Familiarity with computational geometry, 3D scene understanding, or spatial data processing.
• Experience with graph neural networks (PyTorch Geometric, DGL).
• Background in time-series modeling for forecasting and anomaly detection.
• Knowledge of generative AI architectures (diffusion models, transformers, VAEs, GANs).
• Experience with reinforcement learning or multi-objective optimization.
• Contributions to open-source ML projects or publications at relevant venues (NeurIPS, ICML, CVPR).
• Exposure to construction workflows, building codes, or the AEC project lifecycle.