This position will sit within a company that is pioneering a new era of Biomedicine!

Role Overview:

• GPU Cluster Management: Architect, deploy, and sustain high-performance GPU clusters, ensuring they are stable, reliable, and scalable. Oversee and manage cluster resources to maximize efficiency and utilization.
• Distributed/Parallel Training: Apply distributed computing techniques to facilitate parallel training of extensive deep learning models across multiple GPUs and nodes. Optimize data distribution and synchronization for faster convergence and reduced training times.
• Performance Optimization: Enhance GPU clusters and deep learning frameworks to achieve peak performance for specific workloads. Identify and resolve performance bottlenecks through profiling and system analysis.
• Deep Learning Framework Integration: Work closely with data scientists and machine learning engineers to incorporate distributed training capabilities into the company's model development and deployment frameworks.
• Scalability and Resource Management: Ensure GPU clusters can scale effectively to meet growing computational demands. Develop strategies for resource management to prioritize and allocate computing resources based on project needs.
• Troubleshooting and Support: Diagnose and resolve issues related to GPU clusters, distributed training, and performance anomalies. Provide technical support to users and efficiently resolve technical challenges.
• Documentation: Develop and maintain documentation on GPU cluster configuration, distributed training workflows, and best practices to facilitate knowledge sharing and smooth onboarding of new team members.

Qualifications:

• Master's or Ph.D. in computer science or a related field, with a focus on High-Performance Computing, Distributed Systems, or Deep Learning.
• Over 2 years of proven experience in managing GPU clusters, including installation, configuration, and optimization.
• Strong expertise in distributed deep learning and parallel training techniques.
• Proficiency in popular deep learning frameworks such as PyTorch, Megatron-LM, and DeepSpeed.
• Programming skills in Python and experience with GPU-accelerated libraries (e.g., CUDA, cuDNN).
• Knowledge of performance profiling and optimization tools for HPC and deep learning.
• Familiarity with resource management and scheduling systems (e.g., SLURM, Kubernetes).
• Solid background in distributed systems, cloud computing (AWS, GCP), and containerization (Docker, Kubernetes).
• Currently or previously holding a Staff or equivalent title | Currently sitting within a Senior leveled title for 3+ years

The company will provide a relocation package for candidates open to relocate!