Jobs · Washington

Senior Applied ML Engineer

Paradigm · Seattle, WA · Yesterday
HybridFull-time

About the role

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior Applied ML Engineer to be part of revolutionizing these industries.

Responsibilities

  • Develop and optimize CNN and LLM-powered models for computer vision, document extraction, and automated construction workflows.
  • Prototype, fine-tune, and assess models for NLP tasks such as classification, entity recognition, and summarization of construction data.
  • Build scalable ML pipelines and backend services that integrate into production-grade agents and digital platforms.
  • Drive the end-to-end ML lifecycle: from experimentation and training, to deployment, monitoring, and continuous improvement.
  • Integrate retrieval, ML, and rules-based methods to deliver reliable, explainable, and supportable features.
  • Collaborate closely with product managers, software engineers, and construction domain experts to solve real-world challenges with measurable business impact.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
  • 5+ years of experience designing and deploying applied ML systems at scale.
  • Experience with computer vision (CNNs, object detection, segmentation) and natural language processing (LLMs, embeddings, transformers).
  • Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
  • Experience with ML Ops platforms and deploying ML systems into production (MLflow, Kubeflow or equivalent).
  • Experience with APIs, CI/CD pipelines, cloud platforms (AWS/Azure/GCP).
  • Ability to clearly communicate technical concepts to both engineers and non-technical stakeholders.
  • Experience applying ML in construction, CAD/BIM, architecture, or digital twin platforms is preferred.
  • Familiarity with graph-based retrieval, RAG pipelines, or multimodal ML is preferred.

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