City Detect harnesses AI and computer vision to revolutionize urban management by using sensors to map the physical world and help communities respond to issues and track longitudinal changes. Founded in 2021, we recently raised a Series A and are actively partnering with major municipalities across Texas, California, Florida, and beyond to create cleaner, safer, and more livable cities. We value responsible practices, cutting-edge innovation, and collaborative community partnerships as we scale our platform and shape the future of urban living through technology.
Role Overview
We're seeking a Computer Vision AI Engineer with deep experience in transformers, generative models, and vision-language models (VLMs) to push City Detect's products beyond traditional object detection. You'll fine-tune, deploy, and maintain multi-modal models that combine visual and language understanding to deliver intelligent, scalable solutions across heterogeneous real-world environments.
What You'll Do
Fine-tune and deploy vision-language models (VLMs) and object detection models for production use cases
Design and maintain end-to-end pipelines for multi-modal model training, evaluation, and inference in Python
Develop prompt engineering strategies, RAG architectures, and other techniques to maximize model performance
Evaluate model outputs systematically and build feedback loops for continuous improvement
Quantize large transformer models to improve model efficiency
Stay current with rapid advances in transformer architectures, fine-tuning methods, and multi-modal research
Requirements
Candidates without 1+ years of computer vision experience will not be considered, with a strong preference for object detection
2+ years of professional experience working with transformer-based architectures
2+ years of hands-on experience fine-tuning and deploying multi-modal models (VLMs)
Strong experience with LLMs — fine-tuning, inference optimization, and production deployment
Proficiency in Python for model development, training, and deployment (2+ years)
Experience with deep learning frameworks such as PyTorch or TensorFlow
Solid understanding of attention mechanisms, tokenization, transfer learning, and generative model fundamentals
Proven experience taking models from experimentation through production-ready deployment
Nice to Have
SQL proficiency for querying detection results, labeling metrics, or model performance data
Strong preference: experience with roadside or infrastructure object detection (signs, signals, debris, pavement markings)
Background in GovTech, public sector, or smart city projects
Experience in automated driving, ADAS, or autonomous vehicle perception systems
Familiarity with model-assisted labeling, active learning, or human-in-the-loop workflows
Experience with edge deployment or model optimization (TensorRT, ONNX, quantization)
Numbers & Facts
Location
Tuscaloosa, Alabama
Skills
Artificial Intelligence (AI)unmatched
Automotive Automationunmatched
Autonomous Driving Systemsunmatched
Computer Visionunmatched
Continuous Improvementunmatched
Data Modelingunmatched
Deep Learningunmatched
Governmentunmatched
Metricsunmatched
Modeling Languagesunmatched
Object Modelingunmatched
Performance Managementunmatched
Performance Modelingunmatched
Python Programming/Scripting Languageunmatched
SQL (Structured Query Language)unmatched
Smart Citiesunmatched
Strategic Planningunmatched
Team Playerunmatched
Use Casesunmatched
🎯
Be found by employers
5,500+ employers search our resume database daily. Add yours to get found by recruiters looking for candidates like you.
Level up your application
Professional resume templates
Browse dozens of recruiter approved resume templates, layouts and formats. Choose your favorite and make it your own in minutes.