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Applied Machine Learning Engineer
2 weeks ago
~SiteRecon is a B2B SaaS platform transforming property measurements for landscapers and snow removal contractors in the $176B US market. Using advanced AI on high-resolution aerial imagery, we've reduced measurement time from 1 week to 1 day, aiming for 1 hour with upcoming AI advancements.
We seek a skilled Applied ML Engineer to evaluate, enhance and/or transform our computer vision infrastructure from traditional CNN architectures to cutting-edge transformer-based models. This role demands expertise in model design, training, and optimization.
A vast aerial imagery database (7 cm GSD) of 500,000+ properties across the U.A ready market of 350+ customers for immediate deployment.
Powerful compute resources for rapid model training.
This is a frontier challenge in computer vision and GIS. While solutions like Meta's SAM offer basic raster segmentation, they lack the precision for creating high-fidelity, topologically consistent vectors essential for practical GIS applications. shadows, tree canopies) and produces "blobs" rather than architect-quality outputs.
By tackling this focused challenge, we aim to develop scalable templates to generalize automated extraction for broader GIS applications, similar to Waymo's strategy in self-driving technology.
Create and maintain training datasets, including data cleaning, augmentation, and validation
Optimize model training processes for distributed computing environments
Implement efficient inference pipelines for production deployment
Strong foundation in computer vision and deep learning fundamentals
Extensive experience training transformer models from scratch
Experience with ONNX or TensorRT model optimization and deployment
Advanced Python knowledge, including multi-threading and multi-processing optimization
Experience with semantic segmentation tasks
Proven track record of handling large-scale data processing
Required Experience
~5+ years of hands-on deep learning experience
~ Track record of successfully deploying computer vision models in production
~ Experience with vision transformer architectures
~ Experience optimizing models for production using ONNX/TensorRT
~ Masters/PhD in Computer Science, Machine Learning, or related field preferred
Desired Qualities
Strong mathematical foundation in deep learning concepts
Experience with model architecture design and optimization
Experience with vision transformers specifically for segmentation tasks
Published research or contributions to open-source computer vision projects
Experience with high-performance computing environments
Background in geospatial data processing
Hands-on experience with model quantization and optimization using ONNX/TensorRT
Experience deploying optimized models in production environments
