Watkins Talent Solutions
We are looking for a talented Machine Learning Engineer with a specialization in Deep Learning and Computer Vision to lead our green industry analysis initiatives. In this role, you will lead the development and deployment of scalable AI solutions for aerial imagery analysis and design of deep learning models capable of analyzing imagery to distinguish between various environmental classes. You will design, train, deploy, and continuously improve AI models that automatically segment and classify high‑resolution aerial imagery as new imagery becomes available. You will also work closely with software engineers and product teams to integrate these models and AI‑powered analytics and decision‑support tools into the company ecosystem.
WE ARE NOT ENTERTAINING CORP-TO-CORP or 3RD PARTY RESOURCES
About the Role Lead the development and deployment of scalable AI solutions for aerial imagery analysis and design of deep learning models capable of analyzing imagery to distinguish between various environmental classes.
Responsibilities
AI Model Development & Optimization : Design, train, and fine‑tune Convolutional Neural Networks and Vision Transformers for semantic segmentation, object classification, and species identification. Build models for identifying vegetation encroachment and develop AI‑driven risk classification and alert systems for vegetation hazards.
Feature Extraction : Create models tuned to identify lawns, tree canopies, shrubs, hardscapes (driveways, roofs), roadways (dirt, concrete, paved), and powerlines.
Data Pipeline : Build pipelines to preprocess large datasets of imagery (normalization, tiling, augmentation, masking). Establish rules and logic for time‑based change detection and monitoring. Implement predictive analysis tools for vegetation growth patterns and maintenance planning.
Optimization : Balance model accuracy with inference speed to ensure efficient processing of large geographic areas. Implement continuous training and model lifecycle management systems, monitor model performance and automate retraining using new datasets. Establish version control, evaluation, and deployment procedures for models.
Collaboration & Documentation : Work closely with GIS analysts, software engineers, product managers, and domain experts. Maintain proper documentation of methodologies, workflows, and technical solutions. Participate in research, testing, and innovation initiatives.
Qualifications
Experience : 3+ years in Data Science or Machine Learning with a dedicated focus on Computer Vision. Strong understanding and demonstrable experience with various forms of AI.
Proficiency in Python and C++ : Strong coding skills with an emphasis on clean, well‑documented, reusable code.
Deep Learning Frameworks : Production-level experience with PyTorch (preferred) or TensorFlow.
Image Analysis : Deep understanding of CNN architectures such as DeepLabV3+ and U‑Net.
Preferred Skills
Geospatial Tools : Experience with satellite or aerial imagery tools (GDAL, ArcGIS, QGIS).
Remote Sensing : Knowledge of vegetation indices (NDVI) and multispectral imaging.
MLOps : Experience with Docker, Kubernetes, or AWS SageMaker.
Cloud Computing : Familiarity with AWS.
Seniority Level Mid‑Senior Level
Employment Type Full‑time
Job Function Design and Information Technology
Industries Software Development
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WE ARE NOT ENTERTAINING CORP-TO-CORP or 3RD PARTY RESOURCES
About the Role Lead the development and deployment of scalable AI solutions for aerial imagery analysis and design of deep learning models capable of analyzing imagery to distinguish between various environmental classes.
Responsibilities
AI Model Development & Optimization : Design, train, and fine‑tune Convolutional Neural Networks and Vision Transformers for semantic segmentation, object classification, and species identification. Build models for identifying vegetation encroachment and develop AI‑driven risk classification and alert systems for vegetation hazards.
Feature Extraction : Create models tuned to identify lawns, tree canopies, shrubs, hardscapes (driveways, roofs), roadways (dirt, concrete, paved), and powerlines.
Data Pipeline : Build pipelines to preprocess large datasets of imagery (normalization, tiling, augmentation, masking). Establish rules and logic for time‑based change detection and monitoring. Implement predictive analysis tools for vegetation growth patterns and maintenance planning.
Optimization : Balance model accuracy with inference speed to ensure efficient processing of large geographic areas. Implement continuous training and model lifecycle management systems, monitor model performance and automate retraining using new datasets. Establish version control, evaluation, and deployment procedures for models.
Collaboration & Documentation : Work closely with GIS analysts, software engineers, product managers, and domain experts. Maintain proper documentation of methodologies, workflows, and technical solutions. Participate in research, testing, and innovation initiatives.
Qualifications
Experience : 3+ years in Data Science or Machine Learning with a dedicated focus on Computer Vision. Strong understanding and demonstrable experience with various forms of AI.
Proficiency in Python and C++ : Strong coding skills with an emphasis on clean, well‑documented, reusable code.
Deep Learning Frameworks : Production-level experience with PyTorch (preferred) or TensorFlow.
Image Analysis : Deep understanding of CNN architectures such as DeepLabV3+ and U‑Net.
Preferred Skills
Geospatial Tools : Experience with satellite or aerial imagery tools (GDAL, ArcGIS, QGIS).
Remote Sensing : Knowledge of vegetation indices (NDVI) and multispectral imaging.
MLOps : Experience with Docker, Kubernetes, or AWS SageMaker.
Cloud Computing : Familiarity with AWS.
Seniority Level Mid‑Senior Level
Employment Type Full‑time
Job Function Design and Information Technology
Industries Software Development
#J-18808-Ljbffr