GPU-Accelerated Computing Infrastructure Manager

6 days ago

Cambridge, Cambridgeshire, United Kingdom Altos Labs Full time
About the Role

We are seeking a highly skilled Systems Engineer and Administrator to join our team at Altos Labs. As a key member of our Scalable Modeling team, you will be responsible for designing, implementing, and managing our high-performance computing systems infrastructure, with a focus on GPU-accelerated computation and high-performance storage.

Key Responsibilities
  • Design, implement, and manage high-performance computing systems infrastructure, focusing on GPU compute capabilities
  • Configure and maintain high-performance networking and storage infrastructure to support low latency, high throughput distributed computation
  • Collaborate with ML research and engineering teams to understand and meet their accelerated computation needs, ensuring next-generation infrastructure support for emerging trends in AI/ML, including foundation models and LLMs
  • Monitor system performance, troubleshoot and address issues to ensure high availability and optimal performance
  • Develop and maintain system documentation, including hardware/software configurations, troubleshooting guides, and operational procedures
  • Conduct training sessions or workshops to educate users on the proper use of scientific computation infrastructure
  • Stay up to date with the latest trends and advancements in HPC and GPU technologies and advise senior leadership on procurement strategies for next-generation hardware and solutions
Requirements
  • Bachelor's degree in computer science, Information Technology, or a related quantitative field
  • Relevant experience in HPC
  • Extensive experience building and optimizing high-performance computing systems
  • Experience with networking and interconnect architectures commonly found in distributed systems (e.g. Infiniband, Mellanox, 100GbE)
  • Experience managing high-performance storage systems (e.g. Ceph, Gluster, Lustre, etc.)
  • Knowledge of HPC system tools and software stacks, such as job schedulers (e.g. Slurm), performance monitoring, and system management
  • Excellent problem-solving skills and the ability to troubleshoot complex system issues
  • Strong communication skills and the ability to work collaboratively with both technical and non-technical team members
Preferred Skills
  • Strong understanding of modern GPU architectures and programming frameworks like CUDA or OpenCL
  • Experience working with NVIDIA Data Center-class GPUs (A100, H100, etc)
  • Experience with deep learning frameworks like TensorFlow or PyTorch is a plus
  • Familiarity with foundation models and their computational requirements
  • Familiarity with NVIDIA Enterprise AI software platform
What We Offer

At Altos Labs, we offer a competitive salary range for this role, with a base salary of £105,400 - £133,300 in Cambridge, UK. Exact compensation may vary based on skills, experience, and location.

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