Lead ML Engineer

Job DescriptionSenior ML EngineerLocation: San Francisco, CA RemoteEngineering & TechnologyRole OverviewOwn the machine learning function at Zero RFI — building, deploying, and continuously improving models for the construction intelligence platform. Architect production ML systems, lead a growing team, and work at the intersection of deep learning, structured construction data, and real-world workflows. This is a production-focused, technical lead role.Key ResponsibilitiesML Engineering & Production SystemsDesign, build, and deploy end-to-end ML pipelines (data ingestion to production serving) for AEC-specific use cases: document intelligence, schedule analytics, cost prediction.Architect 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 OwnershipDefine 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 IntegrationPartner 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.RequirementsBachelor'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 QualificationsExperience 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.What You'll GainOwnership of the ML function at a company redefining AI in the built environment ($10T+ industry).Access to unique, high-fidelity construction datasets from live programs.Collaboration with architects, engineers, and construction professionals motivated to use AI.A platform role where models built are core infrastructure.Mentorship from technical and domain leaders.Competitive compensation: $270k–$310k salary, equity, and full benefits.