Machine Learning Engineer

• Build and contribute to RAG system components under senior guidance, with growing autonomy
• Write tests and help build evaluation harnesses
• Write production code across AI, backend services, and data pipelines
• Integrate AI components into backend services and RESTful APIs
• Support deployment of containerized systems to AWS using CI/CD
• Contribute to documentation, runbooks, and client handover materials
• Participate in technical discussions and architectural decisions
• Support model evaluation and investigate and improve failure modes
• Take increasing ownership of components and technical decisions

Junior AI/ML Engineer, GenAI, AWS

• Develop and improve real-world computer vision systems, focusing on aerial image recognition
• Build solutions involving object detection, image segmentation, classification, and pattern recognition
• Train, fine-tune, evaluate, and deploy computer vision and machine learning models
• Experiment with modern vision models, foundation models, and multimodal AI technologies
• Research new AI approaches and evaluate their potential value for the product
• Rapidly build prototypes and validate technical ideas
• Improve model accuracy, reliability, performance, and inference efficiency
• Work with visual data and develop data preparation, training, and evaluation pipelines
• Explore LLMs, embeddings, vision-language models, and other emerging AI technologies
• Use AI coding tools and agents to accelerate development, research, debugging, and prototyping
• Identify ways AI can improve the overall product and user experience
• Take ownership of technical challenges from initial research through implementation and deployment
• Work directly with the client’s team on an all-in-one roofing-professional platform for remote roof measurement, reports, estimates, and material presentations