Summary: Machine Learning Engineer & Software Engineer with 11+ years of experience, MSCS at Georgia Tech (Machine Learning).
Recent work includes a 2D→3D architectural reconstruction pipeline (Detectron2 + Swin Transformers), large-scale predictive modelling for gaming platforms (risk, churn, LTV), and NLP systems processing 1M+ messages/month for a US health-tech company.
This is great and exciting. I happened to be doing some research to build memory-efficient diffusion models. I have not yet built the demo, but looking at a mix of architecture from several papers, IMTalker, SageAttension, FlashVSR, and Sparse VideoGen, with the intention to reduce memory to about 8GB.
The plan was to swap FlashAttention out, and also for an audio driver; SVG could have improved. At 60FPS, I think you are already doing this.
Description: I am a Senior Machine Learning & Software Engineer with 11+ years of experience and a current MSCS student at Georgia Tech. I have worked remotely for US-based companies for the last 3+ years and specialize in bridging the gap between robust backend engineering (Ruby on Rails) and production-grade ML models.
Key Projects & Experience:
• Computer Vision (2D to 3D): Currently building a pipeline to convert 2D architectural floor plans into 3D models using Image Segmentation (Detectron2) and Swin Transformers.
• ML for Gaming: Engineered predictive algorithms for high-volume sports betting and lottery platforms, including models for risk management, user segmentation, Churn Prediction and LTV forecasting.
• NLP at Scale: Architected Deep Learning models for a US-based health tech organization that reduced moderation time by 80% and processed over 1 million messages monthly.
• Content Moderation: Developed CensorX, a multimodal NSFW detection tool using Vision Transformers (ViT) and DistilBERT.
Note on Hiring: I am hireable through a Canadian company (B2B/Contract) or via an Employer of Record (e.g., Globalization Partners), allowing for frictionless onboarding for North American entities.
I have been working on CensorX, a multimodal content moderation set of models. It is from a personal project where I built content moderation in a Discord Bot.
I have open-sourced the fine-tuned models on Hugging Face and am looking for feedback on false positives/negatives in real-world scenarios.
The main exploration has been ablations based on freezing certain layers of the transformers. More work could be explored by tuning other parameters and expanding the datasets.
The Models:
• Image (ViT-B/16): Fine-tuned Vision Transformer achieving 91.9% Accuracy and 0.99 AUC.
o Link: https://huggingface.co/acaciabengo/nsfw_image_detection
• Text (DistilBERT): Binary classifier trained on ~200k samples.
o Focus: Optimized for low-latency inference (<100ms) to fit into real-time chat streams.
o Link: https://huggingface.co/acaciabengo/nsfw_text_detection
How to try it:
1. Self-Host (Free): You can pull the weights directly from Hugging Face and run them in your own container.
2. Managed API (Freemium): I have deployed these exact models as a high-availability API on RapidAPI. There is a free tier for testing. RapidAPI
I am very interested in feedback on:
• Performance
• Access to larger datasets
• Shared experience from people who have handled similar tasks
Thank You