Principal Engineer - Machine Learning / Deep Learning Models

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 into 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 :
• MS or PhD in Computer Science, Electrical Engineering, or related field.
• A strong background in the 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 an 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, and Computer architecture.
• Strong Data structures and Algorithms know-how with excellent C / C++ programming skills.
• Hands-on expertise with PyTorch, TensorRT, and CuDNN.
• Hands-on expertise with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP).
• In-depth understanding of container technologies like Docker, Singularity, Shifter, and 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.

(ref : hirist.tech)