Penyebaran Model Jobs

We are looking for an experienced Senior AI/ML Engineer to join our team and help build advanced AI-driven solutions. The ideal candidate will have a strong background in machine learning, deep learning, and data science, with expertise in deploying models at scale. If you're passionate about tackling complex challenges and working with cutting-edge AI technologies, we want to hear from you! We are looking for an individual, not agency. Scope of work - Collaborate with the team to integrate AI/ML technologies into current projects. - Develop and optimize machine learning algorithms. - Perform ongoing analysis and validation of AI models. Key Responsibilities: ** Fluent English Skill is MUST!!! So if you don't have ENGLISH SKILLs don't apply for this position. Develop a...

I’m building a virtual DeepseekV3 environment that emulates Jet Nano hardware for research and development on machine-learning models. The goal is to give my team a sandbox where we can move seamlessly from data preprocessing and feature extraction through model training, evaluation, deployment, and monitoring—without touching the physical board until we are ready. Here’s what I need: • A reproducible simulation that mirrors Jet Nano’s CUDA-enabled GPU, memory constraints, and I/O. • Containerised tool-chain (PyTorch, TensorRT, cuDNN, etc.) with scripts that cover the full life-cycle: preprocessing, training, hyper-parameter sweeps, evaluation metrics, and a mock-deployment stage that tracks resource usage and latency. • Clear documentation so a...

I’m building a diffusion–based pipeline focused on image generation, and I want the results to be crisp, large-format visuals rather than the low-res outputs many models settle for. Your task is two-fold: first, curate or assemble a fit-for-purpose dataset (I don’t yet have one), and second, train a state-of-the-art z-image diffusion model that consistently produces high-resolution renders. You’ll have freedom in tool choice—PyTorch, TensorFlow, DreamBooth, LoRA or any other modern techniques are fine—as long as the final model can be reproduced from the training scripts you deliver. I expect the usual artefacts: cleaned dataset (with clear licensing notes), training code, model checkpoints, and a concise README outlining hyper-parameters, compute used,...