Chief GPU Computing Specialist

1 week ago

Gandhinagar, Gujarat, India beBeeArchitect Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Senior Architect Position

We are seeking a seasoned expert in designing and implementing cutting-edge GPU computing systems that support demanding deep learning, high-performance computing, and computationally intensive workloads.

This is an exciting opportunity to leverage your expertise in machine learning and computer architecture to drive innovation and growth across our products and services.

As a senior architect, you will be responsible for converting business needs into actionable product goals, optimizing algorithms for performance on the GPU tensor cores, and collaborating with cross-functional teams to drive end-to-end workflows from data curation and training to performance optimization and deployment.

Key Responsibilities:

  • Design and implement groundbreaking GPU computers that run demanding deep learning, high-performance computing, and computationally intensive workloads.
  • Identify architectural changes and/or new approaches for accelerating deep learning models.
  • Help evolve our cloud strategy, capacity modeling, and growth planning across our products and services.
  • Convert business needs into actionable product goals covering workload scenarios, end-user expectations, compute infrastructure, and time of execution.
  • Optimize computer vision algorithms and hardware accelerators for performance and quality KPIs.
  • Optimize algorithms for optimal performance on the GPU tensor cores.

Required Skills and Qualifications:

  • Master's or Ph.D. in Computer Science, Electrical Engineering, or related field.
  • Strong background in deploying complex deep learning architectures.
  • 7+ years of relevant experience in at least a few of the following areas: 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.
  • Excellent C/C++ programming skills, hands-on expertise with PyTorch, TensorRT, CuDNN, and HPC cluster job schedulers such as Kubernetes, SLURM, LSF.
  • Proficient in Python programming, bash scripting, Windows, Ubuntu, and CentOS operating systems.
  • Excellent communication and collaboration skills.

Good to Have:

  • Familiarity with cloud computing architectures.
  • Hands-on experience with software-defined networking and HPC cluster networking.

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