3 Nvlink Jobs

We are seeking software engineers to work on next-generation graphics and computing products. Our charter is to build the most stressful set of applications a GPU or high performance computing server would see in its life cycle. The best candidates will have strong C/C++ programming skills, thorough knowledge of graphics concepts and algorithms, a solid foundation of systems software with emphasis on OS fundamentals, and a deep understanding of current generation PC/hardware/embedded architecture. Excellent communication skills and a dedication to meticulous engineering practices are a requirement. As a system software engineer, you will extensively use your knowledge of operating systems, algorithms, and computer architecture to provide robust and efficient solutions to validate and test next generation processors. What you'll be doing: Working closely with architecture, hardware and driver teams through the product development lifecycle of computing and graphics processors, as well as compute products. Responsible for crafting software tools and infrastructure required for new chip development, validation, and productization. Assess new hardware features and architect manufacturing diagnostic solutions using pre-beta driver extensions. This job will require an understanding of our hardware and software architectures. What we need to see: BTech or MTech degree in one of following areas: Electrical Engineering, Computer Engineering, Computer Science or equivalent experience. 5+ years experience in a related hardware/software position. Strong C/C++ programming skills. Familiarity with PC architecture, and ability to work close to the hardware. Background with PCIE, Nvlink or server product technologies like Infiniband, Ethernet is a plus. Previous experience of working on a large system software code base is preferable. Very strong problem solving and debugging skills. Ability to self-manage, show leadership, and have good interpersonal skills. With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working for us and, due to unprecedented growth, our elite engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.

We are now looking for an experienced software engineer with strong system software and image/video/graphics/multimedia fundamentals with robust C++ skills to contribute to the development of NVIDIA AI for Multimedia components like NVIDIA Maxine & NVIDIA Broadcast a comprehensive suite of applications, SDKs and framework that enables different broadcast streaming features. What You'll Be Doing You will work alongside forward-thinking engineers to implement and optimize software to provide new capabilities in NVIDIA AI for Multimedia components as well as improve the existing features and framework architecture for different GPUs, CPUs and system OS platforms. We'll give you exposure to integrating evolving computer vision SDKs into applications & use cases on different platforms. We would be working on many groundbreaking features and their application that are redefining our industry. We will see features go through the whole cycle - from proof-of-concept to production. Together we will connect with multiple teams in our company, as well as our partners and customers. What We Need To See Proficiency in C++ programming and debugging. You have a knack for full stack development - from implementing frameworks and interfaces to understanding the impact and consequence on the hardware. Passion for optimizing system performance, root causing bottlenecks and optimizing performance on GPU, CPU and memory. Strong knowledge of OS fundamentals and familiarity with device driver stacks. You have the knowledge of usage of at least one graphics or image processing library (Direct3D, Vulkan, OpenGL, OpenCV, etc.) Experience in web streaming technologies like UDP, RTP, secure RTP, gRPC, webRTC. Strong software engineering background and design fundamentals particularly related to creating robust system software. Familiarity with software lifecycle-based processes. Strong communication and analytical skills. Self-motivated, fast to act and eager to learn as well as guide. Bachelor/Master/PhD in Computer Science or Electronics or Electrical Engineering. 6+ years of experience developing systems software on Windows/Linux/iOS. Ways To Stand Out From The Crowd Experience in video conferencing, broadcasting applications and environments. CUDA programming experience. Experience working with camera, mic, speaker devices. Background in working with computer vision, image, video processing systems. Experience of collaborating with geographically diverse multi-functional teams. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most talented and hardworking people on the planet working for us. Are you creative Are you driven not just by the need to know why, but yearn to ask, why not And have a real passion for technology, we want to hear from you! We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Our vision is to transform how the world uses information to enrich life for . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Principal / Senior Systems Performance Engineer Micron Data Center and Client Workload Engineering in Hyderabad, India, is seeking a senior/principal engineer to join our dynamic team. The successful candidate will primarily contribute to the ML development, ML DevOps, HBM program in the data center by analyzing how AI/ML workloads perform on the latest MU-HBM, Micron main memory, expansion memory and near memory (HBM/LP) solutions, conduct competitive analysis, showcase the benefits that workloads see with MU-HBM's capacity / bandwidth / thermals, contribute to marketing collateral, and extract AI/ML workload traces to help optimize future HBM designs. Job Responsibilities: The Job Responsibilities include but are not limited to the following: Design, implement, and maintain scalable & reliable ML infrastructure and pipelines. Collaborate with data scientists and ML engineers to deploy machine learning models into production environments. Automate and optimize ML workflows, including data preprocessing, model training, evaluation, and deployment. Monitor and manage the performance, reliability, and scalability of ML systems. Troubleshoot and resolve issues related to ML infrastructure and deployments. Implement and manage distributed training and inference solutions to enhance model performance and scalability. Utilize DeepSpeed, TensorRT, vLLM for optimizing and accelerating AI inference and training processes. Understand key care abouts when it comes to ML models such as: transformer architectures, precision, quantization, distillation, attention span & KV cache, MoE, etc. Build workload memory access traces from AI models. Study system balance ratios for DRAM to HBM in terms of capacity and bandwidth to understand and model TCO. Study data movement between CPU, GPU and the associated memory subsystems (DDR, HBM) in heterogeneous system architectures via connectivity such as PCIe/NVLINK/Infinity Fabric to understand the bottlenecks in data movement for different workloads. Develop an automated testing framework through scripting. Customer engagements and conference presentations to showcase findings and develop whitepapers. Requirements: Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn. Experience in data preparation: cleaning, splitting, and transforming data for training, validation, and testing. Proficiency in model training and development: creating and training machine learning models. Expertise in model evaluation: testing models to assess their performance. Skills in model deployment: launching server, live inference, batched inference Experience with AI inference and distributed training techniques. Strong foundation in GPU and CPU processor architecture Familiarity with and knowledge of server system memory (DRAM) Strong experience with benchmarking and performance analysis Strong software development skills using leading scripting, programming languages and technologies (Python, CUDA, C, C++) Familiarity with PCIe and NVLINK connectivity Preferred Qualifications: Experience in quickly building AI workflows: building pipelines and model workflows to design, deploy, and manage consistent model delivery. Ability to easily deploy models anywhere: using managed endpoints to deploy models and workflows across accessible CPU and GPU machines. Understanding of MLOps: the overarching concept covering the core tools, processes, and best practices for end-to-end machine learning system development and operations in production. Knowledge of GenAIOps: extending MLOps to develop and operationalize generative AI solutions, including the management of and interaction with a foundation model. Familiarity with LLMOps: focused specifically on developing and productionizing LLM-based solutions. Experience with RAGOps: focusing on the delivery and operation of RAGs, considered the ultimate reference architecture for generative AI and LLMs. Data management: collect, ingest, store, process, and label data for training and evaluation. Configure role-based access control dataset search, browsing, and exploration data provenance tracking, data logging, dataset versioning, metadata indexing, data quality validation, dataset cards, and dashboards for data visualization. Workflow and pipeline management: work with cloud resources or a local workstation connect data preparation, model training, model evaluation, model optimization, and model deployment steps into an end-to-end automated and scalable workflow combining data and compute. Model management: train, evaluate, and optimize models for production store and version models along with their model cards in a centralized model registry assess model risks, and ensure compliance with standards. Experiment management and observability: track and compare different machine learning model experiments, including changes in training data, models, and hyperparameters. Automatically search the space of possible model architectures and hyperparameters for a given model architecture analyze model performance during inference, monitor model inputs and outputs for concept drift. Synthetic data management: extend data management with a new native generative AI capability. Generate synthetic training data through domain randomization to increase transfer learning capabilities. Declaratively define and generate edge cases to evaluate, validate, and certify model accuracy and robustness. Embedding management: represent data samples of any modality as dense multi-dimensional embedding vectors generate, store, and version embeddings in a vector database. Visualize embeddings for improvised exploration. Find relevant contextual information through vector similarity search for RAGs. Education: Bachelor's or higher (with 12+ years of experience) in Computer Science or related field.