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Senior Applied ML Engineer
Job Description
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 toSucceed:
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. Ready to Join? at myparadigm.com/careers/Compensation Range:
$125,250 - $183,700Career Insights for Machine Learning Engineer
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Based on Wisconsin data
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$123,797 / year median in Wisconsin