Principal Engineer - HPC/CUDA/GPU

Key Responsibilities

• 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.
• Provide technical leadership and expertise for project deliverables.
• Leading, mentoring and managing the technical team.

• 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.

• 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 workload

(ref:hirist.tech)