Cudatrabajos

...corre sin interrupciones perceptibles. El proyecto consiste en el desarrollo de un motor de inferencia de alta performance para la detección, clasificación y seguimiento de múltiples clases de objetos en entornos dinámicos complejos, utilizando hardware dedicado. Implementación de arquitecturas de detección (YOLO/RT-DETR) y algoritmos de tracking.• Optimización de modelos para hardware NVIDIA (CUDA/TensorRT).• Fusión de datos provenientes de sensores externos (Telemetría/GPS) con flujos de video 4K.• Desarrollo de lógica de persistencia en bases de datos geoespaciales. • Seniority comprobable en Python y OpenCV. Experiencia en el ciclo completo de vida de modelos de visión: desde ...

...Engineer - Real-time Edge AI (OpenCV, ONNX, CUDA & TensorRT) Busco un Senior Computer Vision Engineer con experiencia demostrable en Edge AI para desarrollar un sistema de asistencia táctica en tiempo real basado en captura de vídeo externa. Desafío Técnico Principal: El sistema debe procesar un flujo de vídeo HDMI, realizar OCR de alta precisión y detección de objetos, y consultar una base de datos local con una latencia end-to-end inferior a 100ms. Stack Tecnológico Requerido: • Lenguaje: Python 3.10+ con arquitectura OOP escalable (Interfaces abstractas). • Visión: OpenCV avanzado y procesamiento de imágenes para OCR de stacks y botes. • Motores de Inferencia: Experiencia obligatoria con ...

Instalar CUDA, Cudnn, PyTorch para proyecto de Python YOLOv5

Necesito instalar y compilar un programa que es público en mi Ubuntu 20.04.

Hola, Mi hijo busca ayuda con un trabajo: Implementar el algoritmo de multiplicación de matrices con números en coma flotante en las librerías paralelas OpenMP, OpenMPI y CUDA utilizando un ordenador sobremesa. Tareas a realizar: - Implementación en un multiprocesador usando OpenMP - Implementación en un multicomputador usando OpenMPI - Implementación en un coprocesador de tipo GPU usando CUDA - Evaluación de prestaciones usando contadores hardware

Hola Diego, Mi hijo busca ayuda con un proyecto, puedes ayudarlo? Implementar el algoritmo de multiplicación de matrices con números en coma flotante en las librerías paralelas OpenMP, OpenMPI y CUDA utilizando un ordenador sobremesa. Tareas a realizar: - Implementación en un multiprocesador usando OpenMP - Implementación en un multicomputador usando OpenMPI - Implementación en un coprocesador de tipo GPU usando CUDA - Evaluación de prestaciones usando contadores hardware

Buenas tardes marlon, hemos hablado recientemente sobre un trabajo de cuda y openmp, si me das garantía de que me lo haces en menos de 10 días te pagaré lo que me propusistes 24 euros. Gracias. Saludos

Necesito que desarrollen un software para mí. Me gustaría que este software sea desarrollado.

tengo que hacer trabajo openmp y cuda

Hola necesito hacer trabajos de c, omp,cuda.

Cuda , programacion paralela

Otra o no estoy seguro Python Solicito programadores con conocimientos en CUDA

Hola José. Necesito una mano para implementar en CUDA una serie de funciones que tengo escritas en C++. Mayormente manipulación de matrices para entrenamiento de redes neuronales, deep learning. La idea es implementarlas en una DLL CUDA (para que se ejecute en la GPU) y llamarlas desde Excel VBA. Estamos hablando de unas 300 lineas de código (una vez eliminados huecos y comentarios). Pásame, por pavor, una estimación o tarifa horaria, si te interesa el trabajo. Gracias.

Hola Juan. Necesito una mano para implementar en CUDA una serie de funciones que tengo escritas en C++. Mayormente manipulación de matrices para entrenamiento de redes neuronales. La idea es implementarlas en una DLL CUDA (para que se ejecute en la GPU) y llamarlas desde Excel VBA. Estamos hablando de unas 300 lineas de código (una vez eliminados huecos y comentarios). Por cierto, somos colegas, yo también soy ingeniero aeronáutico. Pásame, por pavor una estimación o tarifa horaria, si te interesa el trabajo. Gracias.

Hola allenross356, vi tu perfil y me gustaría ofrecerte mi proyecto. Podemos conversar los detalles en el chat. I'd like to have a simple neural network(3 layers) in CUDA 8 using the back propagation.

Necesito convertir un proyecto que tengo de un algoritmo de compresión de texto llamado lzw a CUDA y que tenga un speed up de al menos 10 veces. Referencia que puede ser de utilidad:

...paralelizar el código siguiente que corresponde al método de jacobi, hecho en C/C++ en cada uno de los paradigmas (memoria compartida (openMP), Memoria distribuida (MPI) y uso hibrido (CUDA). En el código se deben indicar (con comentarios) las partes paralelizadas . En un archivo en pdf, debera decirme como se debe compilar y correr cada archivo, y que tanto esta acelerando su solución con respecto al original que se le envia. (por ejemplo, puede analizar cuanto tiempo se demora en ejecutarse en la versión secuencial que le envío vs cuanto tiempo en cada una de las versiones paralelas (openMP, MPI, cuda). Puede ser en una gráfica de comparación. Adjunto el código en un archivo de texto, el códig...

Por favor, regístrate o inicia sesión para ver los detalles.

Tenemos una aplicación y deseamos programar, utilizando C++, OpenCV, CUDA. En la actualidad está desarrollado en ambiente LabView  * Categoría: IT & Programación * Subcategoría: Desktop Applications * Es un proyecto o una posición?: Un proyecto * Actualmente tengo: Tengo el diseño * Experiencia en este tipo de proyectos: Si (He administrado este tipo de proyectos anteriormente) * Disponibilidad requerida: Tiempo completo * Integraciones de API: Otros (Otras APIs) * Plataformas requeridas: Windows

Lead AI / Fullstack Engineer — ...communication. ​Traffic Localization: Optimize routing protocols to maximize performance within the TAS-IX network. ​Candidate Requirements ​AI / ML Engineering: ​Proven experience with End-to-end (E2E) speech models (Moshi, AudioLM, or similar). ​Deep proficiency in PyTorch and Transformer architectures. ​Hands-on experience in Fine-tuning LLMs/S2S models for new language groups. ​Expertise in CUDA 12.x and NVIDIA optimization libraries. ​Fullstack Development: ​Expert-level knowledge of WebRTC / WebSockets for real-time media streaming. ​Demonstrated experience in developing Telegram Mini Apps (TMA). ​Professional mastery of FastAPI and React / Next.js. ​Strong understanding of the constraints and requirements of Low-latency systems.

...an ASP.NET Core 9 API up and running on my Windows development machine. The next step is to serve responses from the Ollama Phi3 language model through that API, leveraging an NVIDIA GPU so inference remains fast even under load, and then publish the service so it can be reached publicly from anywhere in the world. Here is what I need: • Configure the Ollama Phi3 model to run on an NVIDIA card (CUDA already installed). • Wire the model into my existing controllers so endpoints stay unchanged for consumers but now call the GPU-accelerated Phi3 instance behind the scenes. • Package everything for production—Docker is fine if it simplifies deployment, but a native Windows service is equally acceptable. • Provide brief, step-by-step documentation so ...

This project requires real GPU computation, correct Bitcoin cryptography handling, and verifiable results. This is not a demo or theoretical project. The program must be fully functional and tested. Only apply if you have proven experience with CUDA, cryptography, or Bitcoin key handling.

...modern GPUs and expose a clean, future-proof API for downstream applications. My end goal is to abstract away vendor-specific quirks so a data-scientist, graphics engineer, or researcher can tap into raw parallel power without worrying about whether the machine is running Windows, Linux, or macOS, or whether it ships with NVIDIA, AMD, or Intel silicon. You’re free to recommend the optimal blend of CUDA, ROCm, OpenCL, Vulkan, or even a custom compute layer—what matters is performance, portability, and clean code that’s easy to extend. I’m open to focusing on a single workload first (machine-learning kernels, real-time graphics effects, or heavy scientific simulations) if that helps us validate the core, then scaling outward. Deliverables I’m exp...

Job Title: CUDA Developer Needed – GPU-Accelerated Bitcoin WIF Key Recovery Tool (Verification Required) Project Description: I am looking for an experienced CUDA / GPU developer to build and optimize a high-performance Bitcoin WIF private key recovery program. This project requires real GPU computation, correct Bitcoin cryptography handling, and verifiable results. This is not a demo or theoretical project. The program must be fully functional and tested. Only apply if you have proven experience with CUDA, cryptography, or Bitcoin key handling. Technical Requirements: - Written in C++ with CUDA - Runs on NVIDIA GPUs - Command-line interface (CLI) - Supports Bitcoin WIF (Base58Check) - Supports compressed and uncompressed private keys - Correct che...

Lead AI / Fullstack Engineer — ...communication. ​Traffic Localization: Optimize routing protocols to maximize performance within the TAS-IX network. ​Candidate Requirements ​AI / ML Engineering: ​Proven experience with End-to-end (E2E) speech models (Moshi, AudioLM, or similar). ​Deep proficiency in PyTorch and Transformer architectures. ​Hands-on experience in Fine-tuning LLMs/S2S models for new language groups. ​Expertise in CUDA 12.x and NVIDIA optimization libraries. ​Fullstack Development: ​Expert-level knowledge of WebRTC / WebSockets for real-time media streaming. ​Demonstrated experience in developing Telegram Mini Apps (TMA). ​Professional mastery of FastAPI and React / Next.js. ​Strong understanding of the constraints and requirements of Low-latency systems.

manual intervention. 3. Re-assemble processed frames back into a single clip using FFmpeg (or similar), ensuring temporal consistency—no flicker or dropped frames. 4. Expose a simple CLI command such as: python --input --output --strength 0.7 --seed 42 5. Provide a short README covering environment setup (Python, diffusers / transformers versions, CUDA requirements), example usage, and expected runtimes Acceptance criteria • The script completes a sample without errors and produces visibly live-action styling throughout. • Code is clean, commented, and includes a or environment.yml. Delivery: source code, README, and one converted sample clip produced by your wrapper.

My current web-scraping pipeline is functional but slow, and the data-processing stage in particular is exhausting the GPU. I want to cut total runtime, shrink GPU memory consumption, and have every run automatically log its own performance so I can measure gains over time. You’ll start by profiling the existing Python code (pandas, NumPy, CUDA-accelerated routines), pinpointing the true hotspots. From there, I need refactored or parallelised logic, smarter batching, and any lightweight caching that prevents redundant computation. I’m open to revisiting earlier extraction steps if a quick tweak there will compound the speed-up, but the main brief is processing-level optimisation. Deliverables: • Optimised processing script(s) with clear inline comments • ...

My current web-scraping pipeline is functional but slow, and the data-processing stage in particular is exhausting the GPU. I want to cut total runtime, shrink GPU memory consumption, and have every run automatically log its own performance so I can measure gains over time. You’ll start by profiling the existing Python code (pandas, NumPy, CUDA-accelerated routines), pinpointing the true hotspots. From there, I need refactored or parallelised logic, smarter batching, and any lightweight caching that prevents redundant computation. I’m open to revisiting earlier extraction steps if a quick tweak there will compound the speed-up, but the main brief is processing-level optimisation. Deliverables: • Optimised processing script(s) with clear inline comments • ...

...short written walkthrough covering hardware requirements, model parameters, and tips for further tuning. Acceptance criteria 1. Frame-by-frame identity preservation ≥ 95 % (verified with face-recognition scores). 2. No temporal flicker visible on 30-fps playback. 3. End-to-end generation time under 2× video length on a single high-end GPU. Tech stack keywords: PyTorch, TensorFlow, FFmpeg, CUDA, Google Colab, facial-landmark detection, GAN inversion. Roadmap beyond this delivery Once the core system is proven, I plan to expand into other AI-driven video features—scene synthesis, automated dubbing, even real-time object tracking—so clean, well-documented code is essential for future extension. Ready to start as soon as we agree on the approach, and ...

...- Auto-sync narration and visuals - Options: - voice selection (male/female) - narration speed - background music (optional) - subtitles (optional) Tech Requirements: - Must support OFFLINE mode (local machine) using open-source models (preferred) - Should also support ONLINE mode (server/cloud deployment) - Efficient pipeline (render without crashing) - Works on CPU + GPU if available (CUDA GPU preferred) Preferred Implementation (engineer decides exact tools): - Python backend (FastAPI preferred) - Local model inference pipeline - Video assembly using FFmpeg / MoviePy - Open-source TTS narration (example: XTTS, Piper, Coqui TTS, etc.) - Open-source image generation or whiteboard assets pipeline - LLM for storyboard/script breakdown (open-source model OR cheapest API) ...

...advise on the best hardware stack to achieve the 15-50 person real-time requirement: Cameras: Recommend specific sensors or cameras (e.g., Wide FOV, Global Shutter, or IR-capable for low-light/glare robustness). Processing Units: Advice on edge deployment. Can this run on a Raspberry Pi 5 with an AI Kit, or is an NVIDIA Jetson (Orin/Nano) required? If commodity GPUs are needed, specify minimum VRAM/Cuda core requirements. Kits: Recommend specific "plug-and-play" kits or enclosures suitable for the indoor/outdoor environment described. Final Deliverables Hardware Recommendation Report: Detailed list of suggested cameras, lenses, and processing kits (Raspberry Pi, Jetson, etc.) tailored to this specific use case. Source Code: Ready to plug into a Python environment ...

I’m running ...trade-offs you introduce so I can reproduce and benchmark I already run basic line-profiler and torch-autograd checks, so I’m looking for deeper insights—vectorised ops, smarter batching, async data movement, or architectural tweaks I may have missed. Feel free to use tools like PyTorch Profiler, nvprof, or your preferred optimisers as long as the final instructions remain reproducible in a standard CUDA environment. If that sounds straightforward, let me know your availability and how you’d approach the first pass; I’m ready to share the repo right away.

..., Twilio or Meta API) OR a custom Mobile App (Flutter/React Native) for security staff. Dashboard: A simple web-based or local interface to view live logs, replay detected incidents, and manage sensitivity settings. Technical Requirements: Programming Language: Python. Frameworks: PyTorch, TensorFlow, OpenCV, YOLO (v8/v10), or MediaPipe. Hardware Compatibility: Must be optimized for NVIDIA CUDA cores / TensorRT. Scalability: The code should support multiple camera streams simultaneously. Deliverables: Full Source Code (well-documented). Setup Guide (How to install on the NVIDIA device and connect cameras). A working prototype/MVP demonstrating the detection of basic theft actions. Ideal Candidate: Proven experience in Computer Vision and Action Recognition. Previous ...

I need a Windows-based GPU workstation dedicated to running local large-language-model workflows. I need someone who can walk me through the full setup—hardware , CUDA drivers, PyTorch/TensorFlow installs, plus the extra tools I rely on for text-to-video generation and similar AI workloads. Your first task is to get the machine fully operational: verify BIOS and power settings, install the latest GPU driver stack, configure CUDA/cuDNN, and deploy the core frameworks. From there we’ll layer in local-LLM utilities (e.g., , Ollama) alongside Stable Diffusion or any other video-generation packages I might explore. Clear, repeatable documentation of every step is essential so I can reproduce the environment later. Once the base system is stable, I’d like on...

...3), OCR (paddleocr 2.10.0 on paddlepaddle 3.0.0 / paddlepaddle-gpu 2.6.2), and post-processing with scikit-learn 1.6.0. Although one GPU-ready wheel is present, all processing still executes on the CPU. The goal is full NVIDIA CUDA utilisation across the entire workflow, from frame decoding to final inference. I need you to: • Profile the current code, pinpoint CPU-bound sections, and migrate or rewrite them for GPU execution (CUDA, CuDNN, cuBLAS, or other relevant CUDA-based APIs). • Update or swap libraries where necessary—feel free to recommend faster CUDA-compatible alternatives if they will not break accuracy (e.g., CuPy, TensorRT, NVIDIA Video Codec SDK). • Modify the code so GUI-less batch processing and real-time video run...

...spots a potential anomaly. All processing must happen in real time without introducing perceptible latency to the surgeon’s view. My current hardware outputs standard HDMI and records to DICOM, so your code should sit either between the camera head and the display (FPGA, GPU box, or high-performance PC is fine) or run as a software module on the workstation already attached to the scope. OpenCV, CUDA, TensorFlow, or similarly robust libraries are welcome—just keep licensing constraints clear. Deliverables • Executable or deployable source that enhances image clarity, performs real-time analysis, and triggers automated anomaly detection. • API or integration hooks so I can feed the processed stream back to my recording software. • A concise user gu...

...machine freeze during model training? Welcome to your new digital superpower. I bridge the gap between your ideas and the raw power of Microsoft Azure. I don’t just "rent servers"—I architect secure, high-performance environments so you can focus on building the future. What’s in my secret sauce? GPU Beasts: Access NVIDIA N-Series (V100, A10, T4) for AI/ML. Ready-to-Go Stack: I’ll pre-install CUDA, PyTorch, TensorFlow, or Docker. No more driver headaches! Fort Knox Security: Advanced Firewalls & private VPNs. Your VM stays invisible to the public web. Windows or Linux? I speak both. Whether you need an RDP or an SSH terminal, I’ve got you. The "Date Before You Marry" Trial Not sure if the speed is right? For just $5, ...

...with a brand-new RTX 5090 and need TensorRT installed, tuned and ready to accelerate Stream Diffusion inside TouchDesigner. I haven’t settled on a specific release yet, so I’ll rely on your guidance to pick the most stable, future-proof version (including matching CUDA and cuDNN builds) for this GPU. Here’s what I expect: • Recommend the best TensorRT version for an RTX 5090 Windows environment and explain why it’s the right fit. • Handle the full installation: download packages, configure environment variables, and verify driver / CUDA compatibility. • Prove the install works by running a sample inference, then confirm TouchDesigner can see the TensorRT engine for Stream Diffusion. • Leave me with a concise, step-by-step recap ...

...a brand-new RTX 5090 and need TensorRT installed, tuned and ready to accelerate Stream Diffusion inside TouchDesigner. I haven’t settled on a specific release yet, so I’ll rely on your guidance to pick the most stable, future-proof version (including matching CUDA and cuDNN builds) for this GPU. Here’s what I expect: • Recommend the best TensorRT version for an RTX 5090 Windows environment and explain why it’s the right fit. • Handle the full installation: download packages, configure environment variables, and verify driver / CUDA compatibility. • Prove the install works by running a sample inference, then confirm TouchDesigner can see the TensorRT engine for Stream Diffusion. • Leave me with a concise, step-by-step rec...

Looking for developer who can work on below requirment . Lead design and implementation of GPU computers for deep learning; optimize algorithms and mentor team Must have key skills cuda,c++,Gpu Programming Other key skills Parallel Computing,Opengl,Opencl Job description What you’ll do CUDA is a must JD For Senior / Lead Engineer (HPC GPU):- As a Senior / Team Lead (HPC) you will provide leadership in designing and implementing groundbreaking GPU computers that run demanding deep learning, high-performance computing, and computationally intensive workloads. We seek an expert to identify architectural changes and/or completely new approaches for accelerating our deep learning models. As an expert, you will help us with the strategic challenges we encounter, includi...

...virtually no perceptible delay. The tool must lock on to faces accurately, track expressions, match lighting and color, and render the composite at a stable frame rate suitable for streaming or studio recording. The core pipeline should include high-resolution face detection, landmark tracking, real-time inference with a modern GAN or transformer model, and seamless blending. Feel free to lean on CUDA-accelerated TensorFlow or PyTorch, OpenCV for image I/O, and any efficient post-processing libraries you trust—what matters is rock-solid performance and visual fidelity. I want the interface to be simple: a preview window, a slot to load or capture the target face, quick toggles to enable/disable tracking, and an option to record or pipe the output to a virtual camera devic...

...deliver must install and operate smoothly in that environment without the usual Linux-only work-arounds that most Jetson guides assume. Here is what I need from you: build a lightweight, fully-functional miner that recognizes the Jetson Nano’s CUDA-capable GPU, connects to any standard Bitcoin pool I specify, and begins hashing immediately after a one-time setup wizard. The setup flow should auto-detect the board’s hardware, prompt for the pool URL, wallet address, and worker name, then save those settings for future boots. Key technical expectations • CUDA acceleration out of the box—no manual library hunting. • Clean, single-click installer for Windows 11 on ARM. • Real-time dashboard showing hash rate, accepted/rejected shares, p...

I have a project that should work with ComfyUI / WAN2 set-up and now need to turn it into approximately 15–20 minutes of finished, classroom-ready video. We have text...• Final MP4s play without glitches on standard players • Everything is handed over within the agreed, ASAP timeline • ComfyUI API We have GPU Server ready with following config Server Configuration Intel Dual XEON E5-2697v4 CPU Cores: 18 RAM: 256GB DDR4 GPU: 3 x Nvidia Quadro RTX A5000 (3GPU) STORAGE: 240GB SSD (Boot) + 2TB NVMe + 8TB SATA (10TB) GPU Specifications Microarchitecture: Ampere CUDA Cores: 10,752 Tensor Cores: 336 GPU Memory: 24GB GDDR6 FP32 Performance: 38.71 TFLOPS If you already work with ComfyUI or similar AI video pipelines and can hit these language requirements quickly, l...

...training a convolutional neural network and now I want it running reliably on an AWS EC2 instance. I already have an AWS account and am settled on using EC2 rather than SageMaker or Lambda, so the task is purely about standing up the production environment and proving that the model answers live requests. Here’s what I need: • Spin up and configure an EC2 instance (Ubuntu preferred) with GPU drivers, CUDA / cuDNN, Python, and either TensorFlow or PyTorch—whichever my model requires. • Package the model (saved .h5 or .pt plus any preprocessing code) into a lightweight service—Flask, FastAPI, or another simple REST interface is fine. • Expose a secure HTTPS endpoint behind an AWS load balancer or an Nginx reverse proxy so I can hit /predict wi...

...that must appear in the output: 1. Player positions and movement traces throughout the match 2. Types of shots taken and whether they resulted in winners, forced errors or unforced errors 3. Rally durations paired with their outcomes Technology preferences are Python with OpenCV, YOLO-based detection, pose estimation for finer tracking, and GPU-accelerated processing on AWS or GCP (or a local CUDA setup if you prefer). A clean, well-documented codebase and brief setup script are part of the hand-off. When you reply, please show: • Examples of previous computer-vision or sports-analytics projects you’ve delivered • A concise outline of the approach you’d take for detection, tracking and event logic • Your estimated timeline from kick-off to fir...

...RTX 3090 and centres on machine-learning workloads. The goal is a single, modular codebase able to orchestrate three specific tasks—image recognition, natural-language processing, and predictive analytics—while squeezing every ounce of performance the 3090’s CUDA cores and Tensor cores can provide. Core requirements • Modular architecture so each task (vision, NLP, forecasting) lives in its own plug-in or service yet can share common utilities such as data pipes, logging and GPU memory management. • Native GPU acceleration using CUDA 11.x (cuDNN, NCCL, TensorRT or comparable optimiser) with fall-backs abstracted cleanly for future upgrades. • Real-time inference endpoint that exposes a lightweight REST or gRPC API for incoming data a...

...optimization, scheduling, and GPU efficiency Experience with large-scale data processing and dataset pipelines Familiarity with multi-GPU, distributed, or accelerated training frameworks Ability to debug training instabilities, loss issues, and performance bottlenecks Bonus Skills: Experience with custom architectures Prior contributions to ML open-source projects Ability to profile and optimize CUDA workloads Research background in LLMs or generative models Compensation & Recognition $200 for winning competition $400 upon completion of MageV1 Public credit as the Official Trainer of MAGIC and MageV1 on: GitHub HuggingFace Documentation Opportunity for expanded future paid roles as MAGIC evolves Priority consideration for extended research collaborations ### ...

...on small projects that culminate in a functioning robot pipeline—from simulation to deployment on the Orin Nano board. Key areas I’d like to cover: • Setting up Isaac Sim and Isaac Lab environments (Ubuntu, ROS 2, Omniverse). • Building, training, and testing perception models in simulation. • Transferring those models to real hardware and optimizing them for Jetson. • Best practices for CUDA acceleration, TensorRT, and power management on Orin Nano. • Road-mapping steps to scale the same workflow to AGX and Thor in the future. Deliverable: a structured learning path supplemented by live sessions, example code, and working demos that run on my Jetson Orin Nano. If you’ve successfully shipped robots or AI applications on Nvidi...

...analytics. The system will ingest RTSP/ONVIF camera streams, run real-time AI detection (person, vehicle, intrusion, loitering, unattended objects), generate alerts, store snapshots/clips, and provide dashboards, reports, and forensic search. A detailed FRD is ready. Required Skills: Real-time video processing (RTSP, GStreamer, FFMPEG, ONVIF) AI/Computer Vision (YOLO, TensorRT, DeepStream, OpenVINO, CUDA) GPU-accelerated inference pipelines Multi-tenant SaaS backend (Node.js / Python / Go) Cloud deployment (AWS/GCP) Databases: PostgreSQL/MongoDB, Redis Frontend: React or Vue Experience building similar video analytics systems is mandatory Who Should Apply DO NOT APPLY if you don’t have previous experience in video analytics / AI surveillance systems. ONLY APPLY if you...

Hey! I am a filmmaker commissioning the development of a high-performance video effects plugin for Adobe Premiere Pro. I require this to be developed in C++ using the Adobe (AE) SDK, with a mandatory focus on GPU acceleration (Metal/CUDA) to ensure real-time performance within Premiere Pro's 32-bit float color pipeline. This plugin is specified to combine four distinct and highly controllable cinematic effects: Bloom, Glow (Mist), Halation, and Curated Grain. 1. Core Functionality & Rendering Pipeline The plugin must implement the following advanced effects pipeline: Bloom (Atmosphere): A soft, full-screen diffusion effect applied additively to the scene, designed to lift the overall atmospheric feel. This effect should slightly elevate shadows/midtones to enhance diffus...