ZeroRFI

ZeroRFI

Lead ML Engineer

San Francisco, CA · Principal

Sponsorship not specified$270k-$310kDetected 19 days ago
PythonCode ReviewVector DatabasesAWSAzureCloud PlatformsDockerKubernetesCI/CDMachine LearningDeep LearningPyTorchscikit-learnPandasNumPyData AnalysisData EngineeringNLPComputer VisionLLMsRAGMLOpsStatisticsA/B Testing

About the role

  • You will ship models into production, measure their impact on active construction programs, and iterate fast.
  • This is a rare opportunity to apply state-of-the-art ML to one of the world's most data-rich and underserved industries.

Responsibilities

  • 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.
  • 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.
  • 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.

Requirements

  • Bachelor's or Master's degree in Computer Science, AI/ML, Statistics, Computational Engineering, or a related field (or equivalent practical experience).
  • 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).
  • Experience with MLOps tooling: experiment tracking (W&B, MLflow), CI/CD for ML, containerization (Docker), and orchestration (Kubernetes or ECS).
  • Excellent communication skills: ability to explain model behavior, limitations, and tradeoffs to both technical teams and non-technical AEC stakeholders.

Nice to have

  • Experience with AEC data types: BIM/IFC schemas, construction schedules (P6, MS Project), RFI/submittal logs, cost databases, or CAD/drawing formats (DWG, PDF).
  • 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.
  • Ownership of the ML function at a company redefining how intelligence is applied to the built environment - a $10T+ global industry that is dramatically underserved by modern AI.
  • Direct access to unique, high-fidelity construction datasets: RFI logs, submittals, schedules, cost databases, BIM models, and reality capture data from live programs.
  • Mentorship from technical and domain leaders who have operated at the intersection of construction and technology across large-scale programs.

Skills

  • ability to explain model behavior, limitations, and tradeoffs to both technical teams and non-technical AEC stakeholders.
  • Familiarity with computational geometry, 3D scene understanding, or spatial data processing (Open3D, trimesh, PointNet++, or similar).
  • Contributions to open-source ML projects or published work in relevant venues (NeurIPS, ICML, CVPR, or applied domain conferences).
  • Collaboration with architects, engineers, and construction professionals who are genuinely motivated to use AI - not just evaluate it.
  • Competitive compensation between of 270-310k salary, equity, and the full Zero RFI benefits package.

Compensation

  • Competitive compensation between of 270-310k salary, equity, and the full Zero RFI benefits package.

Benefits

  • Develop computer vision systems for construction drawing analysis, defect detection from site photography, and progress monitoring from reality capture data.
  • Implement graph neural networks and geometric deep learning models for BIM/IFC data analysis, spatial coordination, and MEP system optimization.
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This listing is sourced directly from ZeroRFI's careers page and normalized into a canonical job model.