227 Pytorch Jobs in Uttar Pradesh - Page 10

Institute of Information Science Postdoctoral Researcher 2 Person The Computer Systems Laboratory - Machine Learning Systems Team Focuses On Research Areas Including Parallel And Distributed Computing, Compilers, And Computer Architecture. We Aim To Leverage Computer System Technologies To Accelerate The Inference And Training Of Deep Learning Models And Develop Optimizations For Next-generation AI Models. Our Research Emphasizes The Following Job DescriptionUnit Institute of Information ScienceJobTitle Postdoctoral Researcher 2 PersonWork Content Research on Optimization of Deep Learning Model Inference and Training AI Model Compression and Optimization Model Compression Techniques (e.g., Pruning And Quantization) Reduce The Size And Computational Demands Of AI Models, Which Are Crucial For Resource-constrained Platforms Such As Embedded Systems And Memory-limited AI Accelerators. We Aim To Explore AI compiler: deployment methods for compressed models across servers, edge devices, and heterogeneous systems. High performance computing: efficient execution of compressed models on processors with advanced AI extensions, e.g., Intel AVX512, ARM SVE, RISC-V RVV, and tensor-level accelerations on GPUs and NPUs. AI Accelerator Design We aim to design AI accelerators for accelerating AI model inference, focusing on software and hardware co-design and co-optimization. Optimization of AI Model Inference in Heterogeneous Environments Computer Architectures Are Evolving Toward Heterogeneous Multi-processor Designs (e.g., CPUs + GPUs + AI Accelerators). Integrating Heterogeneous Processors To Execute Complex Models (e.g., Hybrid Models, Multi-models, And Multi-task Models) With High Computational Efficiency Poses a Critical Challenge. We Aim To Explore Efficient scheduling algorithms. Parallel algorithms for the three dimensions: data parallelism, model parallelism, and tensor parallelism. Qualifications Ph.D. degree in Computer Science, Computer Engineering, or Electrical Engineering Experience in parallel computing and parallel programming (CUDA or OpenCL, C/C++ programming) or hardware design (Verilog or HLS) Proficient in system and software development Candidates With The Following Experience Will Be Given Priority Experience in deep learning platforms, including PyTorch, TensorFlow, TVM, etc. Experience in high-performance computing or embedded systems. Experience in algorithm designs. Knowledge of compilers or computer architectureWorking EnvironmentOperating Hours 8:30AM-5:30PMWork Place Institute of Information Science, Academia SinicaTreatment According to Academia Sinica standards: Postdoctoral Researchers: NT$64,711-99,317/month. Benefits include: labor and healthcare insurance, and year-end bonuses. Reference Site 洪鼎詠網頁: http://www.iis.sinica.edu.tw/pages/dyhong/index_zh.html, 吳真貞網頁: http://www.iis.sinica.edu.tw/pages/wuj/index_zh.html Please Email Your CV (including Publications, Projects, And Work Experience), Transcripts (undergraduate And Above), And Any Other Materials That May Assist In The Review Process To The Following PIs Acceptance MethodContacts Dr. Ding-Yong Hong Contact Address Room 818, New IIS Building, Academia Sinica Contact Telephone 02-27883799 ext. 1818Email dyhong@iis.sinica.edu.tw Required Documents Dr. Ding-Yong Hong: dyhong@iis.sinica.edu.tw Dr. Jan-Jan Wu: wuj@iis.sinica.edu.twPrecautions for application DatePublication Date 2025-01-20Expiration Date 2025-12-31

Profile : Machine Engineer Experience : 2 To 6 Years Requirement Python, pandas, NumPy, MySQL Data Visualization, Matplotlib, Seaborn, Data Cleaning Deep Learning : ANN, CNN, DNN, Back Propagation, TensorFlow 2.x, Keras Web scraping : various library Natural Language processing : Understanding, representation, classification & clustering NLTK,BOW, TFIDF, word2vec Machine Learning : Supervised, Unsupervised (All Algorithm), etc Location : Noida Sector 63 (Work From Office) Working Days : 5 Job Description The Machine Learning Lead will oversee the full lifecycle of machine learning projects, from concept to production deployment. The role requires strong technical expertise and leadership to guide teams in delivering impactful AI-driven solutions. Key Responsibilities Design and implement scalable ML models for various business applications. Manage end-to-end ML pipelines, including data preprocessing, model training, and deployment. Fine-tune foundation models and create small language models for deployment on AWS Inferentia and Trainium. Deploy ML models into production environments using platforms such as AWS. Lead the implementation of custom model development, ML pipelines, fine-tuning, and performance monitoring. Collaborate with cross-functional teams to identify and prioritize AI/ML use cases. Required Skills Expertise in ML frameworks like TensorFlow, PyTorch, and Scikit-learn. Strong experience deploying ML models on cloud platforms, especially AWS. In-depth knowledge of SageMaker and SageMaker Pipelines. Familiarity with RAG-based architectures, agentic AI solutions, Inferentia, and Trainium. Advanced programming skills in Python with experience in APIs and microservices. Exceptional problem-solving abilities and a passion for innovation. (ref:hirist.tech)