Pytorch jobs
I'm seeking an experienced AI developer to create a computer vision model focused on detecting people. The model will need to function effectively in both indoor and outdoor environments. Key Requirements: - Primary function: Object detection with a focus on people - Adaptable to both indoor and outdoor settings - High accuracy an...Adaptable to both indoor and outdoor settings - High accuracy and reliability Ideal Skills and Experience: - Expertise in AI and machine learning - Strong background in computer vision, particularly in object detection - Experience with datasets and training models for varied environments - Proficiency in programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch Please provide examples of similar projects yo...
...purchase history and instantly serves up the pairs they are most likely to buy next. The model can draw on three data streams—user account data, my e-commerce platform records, and any third-party customer datasets I supply—to build a unified profile and surface truly personal suggestions. Here is what the finished job looks like from my side: • A trained model (Python preferred, TensorFlow or PyTorch are both fine) that ingests the above data sources, updates itself regularly, and outputs ranked product recommendations in real time. • An API or embeddable snippet I can drop into the product and home pages to display “You might also like” shoes, along with a lightweight admin panel where I can adjust thresholds and view basic analytics (CTR, ...
...engineers and contribute to technical leadership • Conduct research and implement state-of-the-art AI techniques • Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with REST APIs and Microservices • Excellent analytical an...
...overlays, confidence scores, and report generation—all with strict patient privacy, no storage of originals, and human oversight required. Key Requirements: • Clean React/ frontend with drag-and-drop upload, DICOM viewer (e.g., ), annotation overlays & heatmaps. • Python backend (FastAPI preferred) + secure auth, encrypted file handling, and cloud storage (AWS S3/GCP). • PyTorch/TensorFlow ML models (fine-tune YOLO/U-Net/MONAI on open dental datasets) for multi-label detection/segmentation. • Mandatory: Full anonymization on upload (pydicom/deid), end-to-end encryption, audit logs, compliance-ready (HIPAA/GDPR/APP principles), ethical transparency (e.g., explainability features). • Cloud deployment (AWS/GCP/Azure, serverless ideal). NDA r...
...tangible, and a closing block on “Advanced Machine Learning Techniques” that shows them what’s possible beyond the basics. Because the colleges have explicitly asked for hands-on labs rather than slide-only lectures, your material needs to revolve around live coding, interactive notebooks, and short build-and-test cycles. Required expertise • Solid command of Python, scikit-learn, TensorFlow or PyTorch, plus NLP libraries such as spaCy or NLTK. • An educator’s mindset: you can explain core concepts clearly, scaffold complexity, and troubleshoot student code in real time. • Proven history of running workshops—either academic or corporate—within tight timelines. Deliverables 1. Detailed session plan (3 tracks, 8–10 ...
I need an expert to improve the accuracy of a histopathologic cancer detection model. The current model needs enhancement, and I prefer using algorithm enhancement for this task. Key Requirements: - Improve the model's accuracy ...this task. Key Requirements: - Improve the model's accuracy in detecting cancerous tissues. - Use advanced techniques and methodologies for algorithm enhancement. Ideal Skills and Experience: - Expertise in machine learning and deep learning - Strong background in medical image analysis - Experience with histopathological images - Proficiency in Python and relevant libraries (TensorFlow, Keras, PyTorch) - Familiarity with model evaluation and performance metrics Please provide examples of similar work and a detailed approach to how you would ...
...conversion/refactor: exact same model, exact same results, but with modern syntax, clear separation of concerns, and thorough inline comments. You’ll start from my original scripts and checkpoints, preserve every bit of accuracy, and hand back a fully functioning module (including a simple demo script) that can be installed with pip-installable requirements. Feel free to streamline library calls—TensorFlow, PyTorch, OpenCV, or whatever is currently in place—so long as the final inference output matches the reference I provide. Deliverables • Refactored Python package replicating the current predictions on a supplied test set • README covering setup, dependencies, and usage • Quick comparison report showing identical mAP or better against t...
...recognition beyond the basic horizontal/vertical stripes to oblique, curved, or irregular banding the current code ignores. • Optimize performance: refactor the pipeline for faster image loading, GPU-aware inference, and leaner memory use so it remains responsive on large datasets. Everything runs in Python, so please stay within that ecosystem. You are free to introduce OpenCV, scikit-image, PyTorch, TensorFlow, or other libraries, provided the final solution installs cleanly with a and runs from a single entry-point script or Jupyter notebook. Input will be folders of images; no video or live feed integration is required at this stage, but laying groundwork for future expansion is a plus. I will supply a labeled image set for benchmarking and expect a short report sho...
...research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality • Custom model implementation with commented rationale for design decisions • Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines • Final technical report summarizing methodology, results, and potential publi...
...research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality • Custom model implementation with commented rationale for design decisions • Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines • Final technical report summarizing methodology, results, and potential publi...
Lead AI / Fullstack Engineer — Project "AZIZA" (Voice-to-Voice AI) ​Project Name: AZIZA Format: Project-based / Remote (with access to local GPU clusters) Tech Stack: PersonaPlex (Moshi-based architecture), PyTorch, TensorRT-LLM, FastAPI, WebRTC, Telegram Mini App (TMA). Hardware Location: Uzbekistan & Turkey clusters powered by NVIDIA L40S ​Project Overview ​AZIZA is an innovative multimodal "Speech-to-Speech" (S2S) ecosystem designed to simulate natural human interaction. We are building an AI assistant that seamlessly transitions between roles: an expert tutor (Chemistry, History, Biology), an empathetic companion, and a simultaneous translator. By processing audio tokens directly, the system achieves unprecedented interaction speeds. ​Current Statu...
I need a production-ready object detection model built, trained, and packaged so it runs smoothly on iOS and Android devices, in a modern web browser, and as a lightweight desktop application. The same model should power every platform to keep accuracy and behaviour consistent. You are free to choose the framework you are most comfortable with—TensorFlow, PyTorch, YOLOv8, Detectron2, or another proven library—as long as the final artefacts meet these requirements: • Mobile: optimised builds (e.g. TensorFlow Lite, Core ML, or ONNX) that hit realtime speeds on mid-range phones. • Web: WebAssembly/WebGL or implementation that loads in under three seconds on a standard connection. • Desktop: a small executable or Python app with GPU support when availab...
...Secondary expectations include light cooking, everyday chores, and fluid, human-like movement so the machine blends safely into family life. I am looking for an end-to-end solution covering: • Hardware: anthropomorphic frame, compliant actuators, force-torque sensors, depth & RGB cameras, and medical-grade vitals sensors. • Software: ROS-based control stack, SLAM for navigation, TensorFlow/PyTorch models for vision and speech, and a secure mobile dashboard. * Ability to learn and adapt. * Chatgpt integration. * Human-like skin and features. * Ability to ship to usa. Ability for owner to pack in a box and ship to Philippines easily. • Safety & compliance: redundant fail-safes, IEC/ASTM child-safety standards, and a hygiene-certifiable mate...
The project centres on building a production-ready text-classification pipeline that leverages modern deep-learning techniques. I have a labelled dataset and need end-to-end code that ingests the text, handles cleaning and tokenisation, and trains an accurate classifier. Python is the preferred language; using PyTorch, TensorFlow or another mainstream framework is fine as long as the solution is reproducible and easy to extend. Key deliverables: • Well-commented source code (data loading, model, training loop, evaluation) • Clear instructions to run training on a fresh machine (README or notebook) • Metrics report showing accuracy, precision, recall and F1 on a held-out set • Exported model weights and a small inference script or API endpoint for batch pre...
...stack/Python developer. You will be helping us create and implement the following: • An adoption roadmap that ties specific AI capabilities to each stage of our workflow and project milestones, showing where automation, prediction, or generative content delivers the most value. • A reasoned “why this, not that” selection of tools—think Hugging Face transformers versus OpenAI GPT-4, TensorFlow or PyTorch for model training, spaCy for NLP, Vision APIs for image tasks—plus rapid prototypes that prove the choice. • Drop-in reference implementations or micro-services that slot straight into our existing Node/Express back end. • Plain-language docs and two-minute screen-share videos so community groups, national NGOs, and global partne...
I need a production-ready object detection model built, trained, and packaged so it runs smoothly on iOS and Android devices, in a modern web browser, and as a lightweight desktop application. The same model should power every platform to keep accuracy and behaviour consistent. You are free to choose the framework you are most comfortable with—TensorFlow, PyTorch, YOLOv8, Detectron2, or another proven library—as long as the final artefacts meet these requirements: • Mobile: optimised builds (e.g. TensorFlow Lite, Core ML, or ONNX) that hit realtime speeds on mid-range phones. • Web: WebAssembly/WebGL or implementation that loads in under three seconds on a standard connection. • Desktop: a small executable or Python app with GPU support when availab...
...levels—Admin, Standard user and Guest—each with appropriate permissions for running detections, reviewing results and managing data. Please structure the code so that REST endpoints are cleanly separated; this will let me expose the following Android-ready APIs later on: live-video analysis, image-file analysis and retrieval of disease-history logs. Deliverables • Python inference engine (TensorFlow/PyTorch + OpenCV acceptable) optimised for Raspberry Pi 5 • Django project with the described role system, templates and REST endpoints • Model-training notebook or script plus labeled dataset reference • Setup script or Dockerfile for one-step deployment on a fresh Pi • Brief README covering install, usage and endpoint documentation Ac...
...messages. We need a strong developer to implement the end-to-end pipeline: dataset ingestion, synthetic instruction generation, LoRA fine-tuning, validation harness, and local deployment with an OpenAI-compatible API. Target compute is on-prem GPU hardware (DGX Spark class). Optional experiments can run on Google Vertex AI credits. [Required Skills] - LLM fine-tuning: LoRA/QLoRA, quantization, PyTorch, Transformers, Unsloth preferred - Python data pipelines: JSONL, filtering, reproducibility - Mirth Connect (NextGen Connect): channel XML and Rhino JavaScript transformers - Healthcare interoperability: HL7 v2 (PID, PV1, OBR, OBX), FHIR basics - Docker, Linux, GPU environment setup - Bonus: vLLM, OpenAI-compatible serving, Google Vertex AI [Start Plan] Phase 1 is a vertical slic...
...rescuers can be dispatched quickly. I’m flexible about the imagery source—NASA, ESA, Google Earth, or any other free feed is fine as long as it delivers cloud-free, high-resolution scenes on a daily cadence. You are welcome to mix sources when one is fresher than another. The detector has to work at desert scale, so please build it with an established computer-vision framework (e.g., TensorFlow, PyTorch, YOLO, or a similarly robust model) and output the findings in both human-readable (an image with bounding boxes or a simple web map) and machine-readable form (CSV/GeoJSON with lat/long, time stamp, confidence score). Once I can point the script to a new polygon and receive a list of car and truck coordinates every 24 hours—fully automated, no manual clicks&m...
...intensity and volume to suit beginner through intermediate levels, and outputs a structured weekly routine (exercises, sets, reps, rest, and optional equipment notes). The system should justify its choices in plain language so the end user understands the reasoning behind each exercise block. I’d like the core to be written in Python, preferably leveraging familiar ML libraries such as TensorFlow, PyTorch, or scikit-learn—happy to hear your recommendation. Data sources for exercise selection can come from public, reputable fitness datasets or a curated ruleset you supply. The end result should be easy for me to integrate into a simple web or mobile front end. Deliverables • Clean, well-commented source code for the workout-plan generation engine • A ...
...blinking, lip-sync errors, or artifacts). * Uniform/Badge Recognition: Detect if the person is wearing a police uniform or showing a badge (using object detection like YOLO). * Real-Time Risk Dashboard: * A simple UI that displays a "Trust Score." If the score drops below a threshold, it shows a "SCAM ALERT" warning. Preferred Tech Stack: * Language: Python * ML Frameworks: TensorFlow / PyTorch / Keras * Computer Vision: OpenCV, MediaPipe * NLP: Hugging Face Transformers (BERT/RoBERTa for intent classification) * Interface: Streamlit or Flask (for the demo dashboard) Deliverables: * Source Code (well-commented). * A file for easy installation. * A short demo video showing the system detecting a scam attempt from a sample video file. * Document...
...caching, and Gunicorn as the application server for production; - Inventory Prediction and Purchasing Optimization Built predictive models using Prophet, XGBoost, LSTM, RNN, and PyTorch to forecast inventory levels and identify the optimal reorder point; Predicted future expenditures based on historical consumption, reducing costs from unnecessary purchases and preventing stockouts; Visualized results in Power BI to facilitate decision-making. - Financial Prediction by Department Built a predictive model to estimate revenue and costs per department for the following month; Utilized XGBoost, Prophet, LSTM, RNN, and PyTorch to model different financial behavior patterns; Implemented a data pipeline with PostgreSQL and ELT processes, ensuring efficiency in processing and in...
...raw mobile photos and automatically clean noise, sharpen details, fix color and convert them to 3d printable files. You’ll choose or design the model, train or fine-tune it, then wrap everything in a lightweight API that my mobile team can call in real time (on-device when feasible, cloud fallback when not). You should be completely comfortable with OpenCV plus deep-learning frameworks such as PyTorch or TensorFlow, and you know the trade-offs between traditional filters, GAN-based approaches, and modern super-resolution networks. Experience packaging models for CoreML, TensorFlow Lite or similar mobile runtimes will set you apart. I’m most interested in seeing what you’ve already shipped, so please include past work that proves you can take an image-processin...
...abstract this behind a clean API layer so either provider can be swapped in later. • Send confirmation and reminder messages automatically, with hooks for SMS and email. Tech preferences I am most comfortable reviewing code written in Python, and I already run Docker in production, so please containerise the service. You are welcome to use libraries such as spaCy, Rasa, Dialogflow, TensorFlow or PyTorch—choose what fits the problem, document your reasoning, and make installation reproducible with a single script. What I will accept as “done” 1. A Docker-ised project that I can spin up locally, connect to my calendar, and watch it schedule a demo meeting end-to-end. 2. Clean, commented source code plus a concise README detailing setup, environment ...
...collaboratively train a powerful intrusion-detection model without ever sharing raw traffic logs. Each embassy keeps its packets local, trains an IDS in Python, then ships only the updated weights to a permissioned blockchain that averages them and sends back the enhanced parameters. Key points I need turned into code: • Dual-framework training loops: the local agent must be able to switch between PyTorch and TensorFlow so each site runs whichever stack its engineers prefer. • Attack coverage baked into the dataset pipeline: the IDS must correctly classify Malware, Phishing, and DDoS patterns. Synthetic generation for edge cases is fine as long as the final validation shows clear detection rates for all three. • Smart-contract aggregator: the chain’s sole...
I'm looking for an experienced Python teacher specializing in advanced programming and machine learning. Essential requirements: - Expertise in Python, especially in machine learning - Ability to explain complex concepts clearly - Experience with popular ML frameworks (e.g., TensorFlow, PyTorch) - Flexible scheduling to accommodate my learning pace If you have a strong background in these areas, please apply!
I am rolling out a trio of inter-linked prototypes that tackle pressing urban and environmental challenges while showcasing cutting-edge AI, blockchain, and IoT engineering. First, the AI-driven Smart Traffic System must ingest multi-sensor feeds, apply real-time reinforcement lea...comparing baseline vs. prototype performance Acceptance criteria – Source compiles and runs without modification on the stated platform – ML models reproduce stated metrics using provided data – Voting demo passes independent audit script – Traffic simulation shows ≥ 15 % average wait-time reduction If you need data samples, CAD files, or clarifications on preferred libraries (TensorFlow, PyTorch, ROS2, Hardhat, ), let me know early so I can share the assets and keep...
...Record results, visualise them clearly, and explain the “why” behind the numbers • Iterate on your findings until we have a result worth integrating into production-grade code What you should bring Your application only needs to highlight relevant experience, but that experience must show strong analytical and problem-solving skills. If you are already comfortable in Python, R, TensorFlow, PyTorch or similar stacks, say so—those tools are the basis of most of our work, though deep mastery is less important than the ability to dissect a problem and reason through solutions. Deliverables 1. Reproducible notebooks or scripts for each experiment 2. A concise report explaining methodology, results, and next steps 3. A short hand-off call or recorded...
I’m running a research-grade study on how different transfer-learning strategies affect deep-learning image-classification accuracy and efficiency, and I’d like a PyTorch expert to help me turn the experimental plan into clean, reproducible code and an insightful report. Here’s what I need from you: • A well-structured PyTorch pipeline that can load a few supplied image datasets, swap in multiple pretrained backbones (e.g., ResNet, EfficientNet, ViT) and fine-tune them under several common strategies (feature extractor, full fine-tune, layer-freezing variants). • Training scripts or notebooks with clear logging of hyper-parameters, metrics, and run times so results are comparable across experiments. • A concise written analysis summa...
...and outputs clean, artifact-free H.264/H.265 versions at multiple resolutions. It should be efficient enough for batch processing on a single workstation but scale to a GPU server when required. • Recommend (and set up) an AI model suited to my animation goals, then wire it into the pipeline so I can move seamlessly from encoded footage to AI-augmented renders. Whether you lean on TensorFlow, PyTorch, or OpenCV is up to you—just justify the choice and document how to reproduce it on my side. • Provide concise documentation: command-line flags, filter graphs, model checkpoints, and any tuning parameters. I need to understand why each setting was chosen and how to tweak it for future projects. Acceptance criteria 1. A working FFmpeg script or shell command ...
I need a developer who can take this idea from concept to a working, browser-based product. The core objective is to enrol users with a unique “voice fing...prior to recording. Acceptance criteria • Enrolment and verification must run end-to-end in under five seconds on a typical broadband connection. • Equal Error Rate (EER) ≤ 5 % on a public speaker dataset or a comparable internal test set. • Clear documentation (setup, model training pipeline, API endpoints) plus a Dockerised deployment script. Tech stack is open, but Python (TensorFlow/PyTorch), Node.js, or Rust are all fine as long as you can justify the choice and meet the performance targets. Please outline your proposed approach, libraries you favour (Kaldi, SpeechBrain, etc.), and any simi...
...objective is simple: turn time-consuming manual work into an intelligent process that runs on its own. Because I don’t yet have data or legacy tools in place, we’ll define the workflow together, identify what information needs to be gathered, and then create the logic, models, or rule-based components that best fit. You’ll have full freedom to choose the right libraries—whether that’s TensorFlow, PyTorch, scikit-learn, spaCy, or a leaner approach using pure Python—so long as the end result is reliable, maintainable, and easy for me to extend later. Deliverables 1. Brief discovery call and written workflow outline 2. Prototype demonstrating successful end-to-end automation on sample data you create 3. Clean, well-commented Python code pl...
...I provide the raw market movements, the system should learn patterns and continuously refine its accuracy. Core deliverables: • Predict stock trends ahead of time • Analyze past market movements in an interactive timeline or chart view • Push real-time alerts the moment the model detects a significant event or deviation You are free to choose the most effective stack—TensorFlow or PyTorch for the model, and React Native, Flutter, or another proven framework for the apps—as long as the final build passes App Store and Google Play review. The AI must run efficiently server-side, expose a secure API to the app, and allow me to retrain or fine-tune the model with new data whenever needed. Please outline your proposed architecture, training work...
... The model must learn from three separate data streams—historical wheel outcomes, player betting behaviour logs, and detailed wheel-mechanics data—so it can pick up hidden biases and temporal patterns rather than relying on simple probability tables. Your task is to deliver a trained model plus everything I need to keep it learning as new data arrives. I’m comfortable with Python, TensorFlow/PyTorch, or a lightweight C++/C# inference library; whichever you choose, just expose a clean API that my front-end can call before each spin and return ranked predictions in well under 100 ms. Key deliverables: • End-to-end training pipeline (data cleaning, feature engineering, model training, validation) • Inference module or REST/gRPC service ready to drop...
...Evolutionary Test-Time Compute approach outlined in Jeremy Berman’s posts ( and follow-up). My main goal is stronger generalization, not just memorized accuracy, so the solution must show consistent gains across the public and hidden splits. Everything will run in Python. You can pick whatever supporting libraries you like (PyTorch, JAX, NumPy, Hugging Face, etc.) as long as setup stays lightweight and the code is clearly documented. Core scope • Implement evolutionary prompt search: generate, mutate, rank, and select prompts on-the-fly against ARC tasks. • Automate evaluation: scoring script should mirror ARC’s official rubric so results are directly comparable to the leaderboard. • Track sample efficiency: log compute
...report or dashboard, the assignment requires a fully realised project: working code, clear data-processing steps and concise documentation that I can present and defend. Here is what I need from you: • End-to-end pipeline: data sourcing or simulated dataset, cleaning and transformation, model training, evaluation and basic BI-style insights. • Well-commented Python or R notebooks (TensorFlow / PyTorch welcome) plus any auxiliary scripts. • A short explanatory document (≈5 pages) outlining objectives, methodology, results and how the solution fits within a Business Intelligence context. • Visual artefact: simple dashboard or set of plotted metrics that showcases key findings to a non-technical audience. Deep learning per visione artificiale; &bul...
Lead AI / Fullstack Engineer — Project "AZIZA" (Voice-to-Voice AI) ​Project Name: AZIZA Format: Project-based / Remote (with access to local GPU clusters) Tech Stack: PersonaPlex (Moshi-based architecture), PyTorch, TensorRT-LLM, FastAPI, WebRTC, Telegram Mini App (TMA). Hardware Location: Uzbekistan & Kazakhstan (TAS-IX), clusters powered by NVIDIA RTX 4090. ​Project Overview ​AZIZA is an innovative multimodal "Speech-to-Speech" (S2S) ecosystem designed to simulate natural human interaction. We are building an AI assistant that seamlessly transitions between roles: an expert tutor (Chemistry, History, Biology), an empathetic companion, and a simultaneous translator. By processing audio tokens directly, the system achieves unprecedented interaction spee...
I have collected numerical manufacturing data and need a working large-language-model approach that can accurately predict our experimental outcomes so we can cut material waste on the shop floor. Here’s what I need from you: • Build and train the LLM-powered model entirely in Python (TensorFlow, PyTorch, or similar—whatever you prefer). • Use my numerical datasets as the sole training source; they cover typical manufacturing readings and KPIs. • Validate the model’s performance with clear metrics and a concise report that shows how well the predictions match real results. • Supply clean, well-commented code plus a brief read-me so my in-house engineers can rerun or fine-tune the model later. Acceptance is straightforward: when you...
...comfortable with deep-learning pipelines and modern web development. First, you will fine-tune a large language model so it can deliver domain-specific answers through an API. Then you will create an e-commerce site that sells our digital products while showcasing the model’s capabilities. The model work involves preparing the dataset, selecting an appropriate base model, running the training (PyTorch or Python are fine), and exposing the result through a secure REST or GraphQL endpoint. Clear documentation on hyper-parameters, versioning, and how to reproduce the run is essential. For the website, I want a clean, responsive storefront with a shopping cart, payment gateway integration, and an admin dashboard for product and content management. Whether you choose React + N...
I need a compact electron vite Python-based pipeline that can watch hours of raw footage and automatically carve it into clean rushes. The idea is simple: detect every meaningful scene change or pronounced camera movement, mark the in- and out-points, and hand back an ordered timeline I can drop straight into my editing suite. My tool stack is already defined: PyTorch Video for the deep-learning backbone, PyDetect for proven vision utilities, and OpenCV for the faster classical operations. If you prefer to wrap parts of the workflow in ffmpeg or similar, that’s fine as long as the core logic stays in Python and can be called through a clear API endpoint. Scope • Analyse full-resolution clips, spot scene boundaries and camera pans/tilts/zooms with high recall. •...
...Natural Language Processing. My goal is to understand core NLP concepts, build a small portfolio of projects, and feel confident discussing them in interviews. I already code a little in Python but have no formal NLP background. I’d like to cover the foundations—tokenisation, embeddings, transformers—then progress to hands-on mini-projects using libraries such as spaCy, Hugging Face, TensorFlow or PyTorch. Guidance on best practices for data preprocessing, model selection, evaluation and error analysis is essential, along with tips on how to present this work in a résumé or GitHub repo. Deliverables I have in mind: • A personalised learning roadmap with milestones • Live or recorded sessions focused on theory followed by coding walk-t...
...interface quality, and overall fit. Once I’ve tested your demo, I’ll outline the domain specifics and grant data access. The engagement will revolve around refining your existing model, exposing a clean REST or GraphQL API, and deploying the solution to my cloud environment (AWS or GCP). Solid accuracy, low latency, and well-documented Python code that leverages libraries such as Hugging Face, PyTorch, TensorFlow, or LangChain are all critical. Deliverables: • A publicly accessible link to your working NLP demo • Brief but clear architecture and tech-stack summary • A step-by-step plan to adapt the model to my use case • Final integrated solution with deployment and usage documentation I’ll review submissions on a rolling basis and...
I need an end-to-end neural rendering pipeline that can faithfully reconstruct low-light scenes with NeRF. All training will be image-based and must run entirely in PyTorch. You are free to choose or combine publicly available night-time or low-illumination datasets, as long as the licence allows research use and you clearly cite the source. Here is what I expect at delivery: • A reproducible PyTorch implementation (data loader, network, training loop, rendering code). • Training and inference scripts that run on a single GPU and are easy to adapt to new datasets. • Built-in evaluation that logs PSNR, SSIM and LPIPS after every validation epoch and prints a final summary table. • A short report (markdown or PDF) discussing dataset choice, hyper-pa...
I want to build a full, low-light-oriented NeRF pipeline whose sole goal is to lift image quality. The core of the work is on the model architecture itself; I am happy to keep pre- and post-processing simple as long as the neural radiance field you design excels in dim scenes. What I need from you: • A PyTorch (or JAX/TensorFlow if you strongly prefer) implementation of a NeRF variant tuned for low-illumination data. • Training and inference scripts that I can run on a single-GPU workstation. • A concise evaluation notebook that reports PSNR, SSIM and LPIPS on the validation set so I can judge improvements immediately. • Clear instructions on how to add new scenes or fine-tune further. The work is finished when I can clone the repo, point it at my datase...
...later; please design the pipeline with those hooks in mind. Preferred flow 1. Upload or API push of multiple images. 2. Server chooses or lets me choose an alternative viewpoint/crop (“camera cut”). 3. AI engine processes, saves, and returns one or more high-res JPEGs per source image. 4. Dashboard shows progress, queues, and download links. Tech notes Feel free to lean on Python, TensorFlow/PyTorch, OpenCV, or similar libraries, plus a modern front end (React or Vue) and cloud storage (S3, GCS, or equivalent). Clean, well-commented source code, deployment scripts, and concise setup documentation must accompany the final hand-off. Deliverables (acceptance criteria) • Web portal deployed on a test server and accessible via URL • Batch upload handling at ...
...hands-on computer-vision engineer to turn raw sports footage into structured insight. The system you build will ingest live or recorded matches, lock onto the ball and key players, keep them centered with an intelligent zoom, and raise flags the moment a goal is scored or a significant positional change occurs. I expect you to stitch together proven object-detection or tracking APIs with your own PyTorch, TensorFlow, or OpenCV code so we hit production-grade accuracy without reinventing wheels. The pipeline should: • track players, the ball, and other relevant objects frame-by-frame, • adjust the crop dynamically so the action stays in focus, • recognise high-value events such as goals or point scoring, as well as nuanced player movements and formations, ...
...Troubleshoot and resolve technical issues - Stay updated with the latest industry trends and technologies Requirements: - Proven experience as a Full Stack Developer - Strong knowledge of front-end technologies (React, Angular, Vue, etc.) - Proficiency in back-end frameworks (Node.js, Django, Flask, etc.) - Experience with databases (SQL, NoSQL) - Solid understanding of AI/ML frameworks (TensorFlow, PyTorch, etc.) - Good problem-solving skills and attention to detail - Ability to work independently and as part of a team - Excellent communication skills Preferred: - Prior experience with long-term projects - Familiarity with cloud services (AWS, Azure, GCP) - Knowledge of DevOps practices What we offer: - Long-term collaboration - Flexible working hours - Competitive compensati...
...CRM, or recommendation systems). Conduct feasibility assessments, risk evaluations, and proof-of-concept development to accelerate time-to-market. Provide technical leadership and mentorship to ensure high-quality, maintainable code and architecture. Required Qualifications 8+ years of experience as an AI Architect or similar role, with proven expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, GPT models, RAG systems). Strong background in bridging business needs to technical implementations, demonstrated through successful projects with short development cycles. Proficiency in cloud platforms (AWS, Azure, GCP) for AI deployment, MLOps, and scalable architectures. Experience in data engineering, NLP, computer vision, or predictive modeling relevant to business applicat...
...files—and I have already completed data cleaning, feature extraction, and normalization, so you can dive straight into modelling. Your task is to craft, tune, and ensemble models that out-perform the current leaderboard benchmark, then package everything into a fully reproducible training notebook and a submission-ready inference script. Python with scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, or TensorFlow are all welcome; pick the stack you believe will squeeze out the highest score. Deliverables • Commented training notebook(s) demonstrating data pipeline, model selection, tuning, and validation • Stand-alone inference notebook/script ready for Kaggle submission • README summarising architecture choices, hyper-parameters, CV strategy, an...
