Senior Applied ML Engineer

Job description

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.

We are looking for a Senior Applied ML Engineer to design, implement, and scale machine learning systems that power next-generation construction and digital twin solutions. You will apply advanced ML techniques—ranging from computer vision to large language models—to automate critical workflows such as blueprint understanding, 3D model generation, and materials forecasting. This role blends research, engineering, and domain expertise to deliver practical, production-ready AI systems that transform how homes are designed, estimated, and built.

What You Will Do

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.

What You Need to Succeed

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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Who can apply

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