What You Will Contribute To Altos

The Altos Labs team is seeking a Systems Engineer and Administrator specializing in high-performance (HPC) and GPU-accelerated computing to work closely with the Scalable Modeling team and help manage the scientific compute infrastructure of our organization. This role will lead the design, implementation, and support of our high-performance computing systems, with a focus on GPU-accelerated computation and high-performance storage. Candidates for this role will work closely with our research and engineering teams to operate infrastructure and platforms for large-scale AI/ML model training and inference, ensuring efficient and reliable operation of our HPC infrastructure.

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

Who You Are

Required Qualifications

• Bachelor’s degree in computer science, Information Technology, or a related quantitative field.
• Minimum 8 years with 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

The salary range for San Francisco Bay Area, CA:

• Staff Software Engineer: $215,050 - $290,950

The salary range for San Diego, CA:

• Staff Software Engineer: $202,300 - $273,700

Exact compensation may vary based on skills, experience, and location.

For UK applicants, before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

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