CUDA Jobs

I’m building a ROS 2 pipeline that must reliably spot nuts and bolts in a live camera feed and publish their positions to the rest of my stack in real time. Your job is to create the complete vision-detection module—from model training or fine-tuning through to a clean ROS 2 node that subscribes to an image topic and spits out the detected objects with bounding boxes (or masks) and a confidence score. OpenCV, TensorFlow/PyTorch and any of the common ROS 2 image-transport plugins are all fine as long as the final node runs on Humble and stays GPU-agnostic (CUDA acceleration is a bonus, not a requirement). I already have a test rig with a standard USB camera; if you need specific calibration images I can capture them for you. Please deliver: • Source code for the detection...

I am seeking an expert AI Engineer to develop a high-fidelity Interview Preparation Agent. The goal is to prepare for a Cloud Solution Architect - Azure Security role at Microsoft. The agent must simulate a multi-stage interview loop, evaluate technical/architectural accuracy against Microsoft-specific frameworks (CAF, WAF), and provide actionable coaching. Detailed Scope of Work & Features: Multi-Stage Interview Simulation (Loops): Phase 1: HR/Behavioral: Focus on Microsoft Leadership Principles and Growth Mindset. Phase 2: Technical Deep-Dive: Interactive grilling on Azure IaaS, Security (WAF, Key Vault, Azure AD), Networking, and Kubernetes. Phase 3: System Design & Strategy: Architecture-level discussions focused on "Support for Mission Critical" scenarios. Conte...

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 need a Windows-based desktop application that turns a single still image into an AI-generated video, entirely offline. The workflow should rely on a local Stable Diffusion installation and incorporate the WAN2.6 model to drive the final animation—no external API calls, cloud processing, or telemetry of any kind. Core requirements • Runs natively on Windows 10/11 and installs with minimal user setup. • Accepts at least JPEG files for input; adding PNG or BMP later should remain possible. • Generates a short video (MP4 preferred) by feeding the image through Stable Diffusion and WAN2.6. • Interface must feel intuitive for non-technical users while exposing advanced settings in an “expert” panel. • Conversion speed is critical; please op...

I want to turn raw datasets into clear, data-driven insights through an AI-powered analysis workflow. The goal is a self-contained solution that ingests spreadsheets or database exports, cleans and transforms the information, uncovers meaningful patterns and trends, then delivers the results as shareable files and a concise report or dashboard. You may choose the most effective stack—Python with Pandas, scikit-learn, TensorFlow, or similar—so long as it runs reproducibly on a standard Linux environment. I will supply sample data and outline the business questions once we start; from there I expect you to design and code the pipeline, select suitable algorithms, train and validate any models, and document every step clearly. Deliverables • Well-structured, commented sou...

What You'll Do Design, train, and deploy machine learning models for real-world applications (NLP, computer vision, forecasting, or recommendation systems). Build and optimize scalable ML pipelines using tools like Airflow, Kubeflow, MLflow, or SageMaker. Collaborate with data engineers to ensure clean, reliable data flows from source to model. Implement monitoring, drift detection, and retraining strategies for production models. Prototype new AI capabilities and translate research into production-ready features. Mentor junior engineers and contribute to technical strategy and architecture decisions. Champion best practices in MLOps, model governance, and ethical AI. What You Bring Required: Candidates must be reside in Philippines and other South East Asia. 3+ years of hands-on expe...

I have several software initiatives that require robust, production-ready machine-learning models. I’m looking for an AI engineer who can take a dataset from raw form through model design, training, validation, and deployment, then document the entire pipeline so it can be maintained by my in-house team. You should be comfortable choosing the most appropriate algorithms, handling feature engineering, tuning hyper-parameters, and explaining trade-offs in accuracy, speed, and resource use. Typical tools in my stack include Python, TensorFlow, PyTorch, scikit-learn, and Docker, so experience with these will be valuable. Deliverables • Clean, well-commented code in a Git repo • Reproducible training scripts and environment files • A trained model (or models) saved ...