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We need an operations-focused team member to support daily operations for our construction monitoring platform, VisionBuilder. This is an internal backend role focused on quality assurance and coordination. You'll work with 3D assets from reality captures generated during daily construction site walkthroughs. Using our internal tools and workflows, you'll handle manual alignment, reconstruction QA/QC, and quality assurance of automatic change detection outputs. Requirements: • Strong attention to detail and quality assurance experience • Technical aptitude for learning internal software tools quickly • Experience with 3D asset processing or construction technology preferred • Excellent coordination and communication skills • Ability to handle rapid resp...

I have collected hundreds of standard photographs of hands in many poses and from many people. Nothing is uniform: skin tones, hand sizes, lighting, backgrounds, and even finger counts may vary. The goal is to turn this dataset into a practical analysis tool that can: • calculate the flexion/extension angle at every finger joint in each image • flag when a finger or phalanx is completely absent so that it can be recorded automatically I expect you will combine classical computer-vision preprocessing with a deep-learning model (PyTorch or TensorFlow are fine) and possibly leverage pose-estimation libraries such as MediaPipe or OpenCV for landmark detection. Accuracy matters more than perfection in lighting or background, so robust data-augmentation and domain-adaptation tech...

We want to use the existing data and use other concepts for data augmentation compared to what we have now. Then we want to retrain the machine learning model.

I’m building an NLP-driven, multimodal assistant that accepts text, image, and audio inputs, but its replies still drift into hallucination. The goal is straightforward: sharpen response accuracy so the system stays firmly grounded in fact. Right now the core pipeline is a Hugging Face Transformer model wrapped in a Retrieval-Augmented Generation (RAG) layer. I need you to audit the entire flow, diagnose where and why hallucinations appear, and then apply proven mitigation techniques. That could involve prompt engineering, better retrieval logic, truth-focused data augmentation, fine-tuning, or introducing guard-rail frameworks—whatever combination delivers measurably higher factual precision. Deliverables • A revised model or inference pipeline that demonstrably impro...