CUDA Jobs

I want to see a working proof-of-concept that can watch a live webcam feed in an indoor setting and reliably decide whether someone is merely holding a phone or actively using it. The prototype must process the video stream in real time, recognise the presence of a smartphone, then look for behavioural cues—hand placement, posture and, ideally, gaze direction—to confirm active usage. Whenever the model judges that the phone is being used, it should trigger an audible or visible alarm on the host machine instantly; no other logging or alert channels are required for this first iteration. I am happy for you to choose your preferred computer-vision stack (e.g. OpenCV, MediaPipe, PyTorch, TensorFlow, ONNX) as long as the end result runs on a typical workstation without specia...

We do not accept autobids. We must read the entire post or your bid will be automatically rejected. We are a new AI startup building cutting-edge systems and tooling. We move fast, build fast, and test aggressively. We are looking for a Test Environment Engineer to help manage and troubleshoot our development environments while working alongside our internal AI coding system (Codex) and the system architect (me). This is not a passive role. You will be actively managing the test environment while code is being generated and executed in real time. With the combination of me (the Architect), you (the Test Engineer), and Codex (our AI coder) we can move as fast as a team of ten. The Environment Our stack is primarily: Ubuntu / Linux servers Python-based AI tooling GPU compute nodes ...

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...