Senior Applied ML Engineer

Paradigm

Senior Applied ML Engineer designing scalable machine learning systems for construction industries. Collaborating to automate workflows and enhance operations using advanced ML techniques.

Posted 7/28/2026full-timeIrving • Colorado, Texas, Washington, Wisconsin • 🇺🇸 United StatesSenior💰 $125,250 - $183,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing CNN and LLM-powered models for computer vision and NLP tasks, with a strong focus on building scalable ML pipelines and deploying production-grade systems. Proficient in collaborating with cross-functional teams to deliver impactful solutions in the construction domain.

Highest-signal resume keywords

Computer Vision (CNNs, Object Detection, Segmentation)Natural Language Processing (LLMs, Embeddings, Transformers)Python ProgrammingML Frameworks (PyTorch, TensorFlow, Hugging Face)ML Ops Platforms (MLflow, Kubeflow)

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DeploymentData ClassificationEntity RecognitionModel Fine-TuningScalable ML PipelinesContinuous ImprovementAPIsCI/CD PipelinesCloud Platforms (AWS/Azure/GCP)

Soft Skills

Clear CommunicationCollaboration

Certifications & Qualifications

Bachelor’s or Master’s Degree in Computer Science, Machine Learning, or Related Field

Industry Keywords

ConstructionCAD/BIMArchitectureDigital Twin PlatformsGraph-Based RetrievalRAG PipelinesMultimodal ML

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development