The Role
Fine-tune, deploy, and maintain vision-language and large language models for production. Build end-to-end multi-modal training, evaluation, and inference pipelines in Python, implement prompt engineering and RAG, quantize transformers for efficiency, and create feedback loops to continuously improve model performance in real-world environments.
Summary Generated by Built In
We're seeking an 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.
- Fine-tune and deploy vision-language models (VLMs) and large language 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
- 3+ years of professional experience working with transformer-based architectures
- 2+ years of hands-on experience fine-tuning and deploying multi-modal models (VLMs)
- 2+ years of proven computer vision experience, with a strong preference for object detection
- 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
- SQL proficiency for querying detection results, labeling metrics, or model performance data
- 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)
The base pay range for this role is $100,000 – $130,000 per year.
Skills Required
- 3+ years of professional experience working with transformer-based architectures
- 2+ years of hands-on experience fine-tuning and deploying multi-modal models (VLMs)
- 2+ years of proven computer vision experience, preferably object detection
- Strong experience with LLMs including 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
- SQL proficiency for querying detection results and model performance data
- 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)
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The Company
What We Do
City Detect is an AI-powered platform that helps local governments and non-profits identify and combat urban blight and property degradation. Utilizing vehicle-mounted cameras and computer vision technology, the company automatically assesses the exterior condition of houses across a city. This provides municipalities with actionable data and reports to prioritize urban redevelopment and create cleaner, safer communities.
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