Job
Description
CUDA is a must JD For Senior 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, including compute, networking, and storage design for large scale, high-performance workloads, effective resource utilization in a heterogeneous computing environment, evolving our private/public cloud strategy, capacity modelling, and growth planning across our products and services. As an architect you are responsible for converting business needs associated with AI-ML algorithms in to a set of product goals covering workload scenarios, end user expectations, compute infrastructure and time of execution; this should lead to a plan for making the algorithms production ready Benchmark and optimise the Computer Vision Algorithms and the Hardware Accelerators for performance and quality KPIs. Optimize algorithms for optimal performance on the GPU tensor cores. Collaborate with various teams to drive an end to end workflow from data curation and training to performance optimization and deployment. Mentor and manage the reporting team Skills Required MS or PhD in Computer Science, Electrical Engineering, or related field. A strong background in deployment of complex deep learning architectures . 5+ years of relevant experience in at least a few of the following relevant areas is required in your work history: Machine learning (with focus on Deep Neural Networks), including understanding of DL fundamentals; Experience adapting and training DNNs for various tasks; Experience developing code for one or more of the DNN training frameworks (such as Caffe, TensorFlow or Torch): Numerical analysis, Performance analysis, Model compression and Optimization & Computer architecture. Strong Data structures and Algorithms know-how with Excellent C/C++ programming skills. Hands-on expertise with PyTorch, TensorRT, CuDNN Hand-on expertise with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP) In-depth understanding of container technologies like Docker, Singularity, Shifter, Charliecloud. Proficient in Python programming and bash scripting. Proficient in Windows, Ubuntu and Centos operating systems. Excellent communication and collaboration skills. Self-motivated and able to find creative practical solutions to problems. Good to have Hands-on experience with HPC cluster job schedulers such as Kubernetes, SLURM, LSF. Familiarity with cloud computing architectures Hands-on experience with Software Defined Networking and HPC cluster networking. Working knowledge of cluster configuration management tools such as Ansible, Puppet, Salt. Understanding of fast, distributed storage systems and Linux file systems for HPC workloads.
