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.

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.

What You'll Do

• 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

Requirements

• 3+ 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
• 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)

Benefits

• Eligible for company equity incentive plan
• Fully remote position
• Unlimited PTO
• Health, vision, and dental insurance
• $100 monthly wellness stipend
• Bi-annual team retreat
• Professional development opportunities

What to Expect in Our Hiring Process

Our hiring process is designed to be thoughtful, efficient, and human. Candidates typically move through a short series of interviews over 2–3 weeks, starting with a 30-minute phone screening, followed by one or two technical conversations and a final interview with our CEO.

We focus on cultural alignment, real-world technical understanding, and career goals—not coding puzzles or LeetCode-style tests. You’ll hear back within 24 hours after each stage whenever possible. If an offer is extended, the role begins with a 30-day trial period where you’ll take ownership of a meaningful project and receive clear, ongoing feedback to ensure mutual fit.

Due to regulatory and operational requirements, we are currently only considering candidates based in the United States.