Staff HPC Engineer

Hiring Manager

The HPC Engineering team is part of the AI Compute Platform organization at Biohub, a non-profit research lab committed to open science and open-source AI.

About the Role

We seek a Staff HPC Engineer to help lead the evolution of our advanced computing infrastructure into a next-generation hybrid HPC and AI platform. This role will help shape strategy, architecture, and operations for high-performance computing resources — including cutting-edge GPUs, large-scale storage, and high-speed networks — while enabling transformative science through AI and machine learning at scale.

Opportunity

• Build and support a hybrid HPC-AI environment with large-scale on-prem compute/storage and elastic cloud GPU clusters (Coreweave, AWS, GCP).
• Architect and optimize environments for large-scale AI training and tuning, and low-latency scientific workloads.
• Integrate MLOps and model deployment pipelines into HPC infrastructure, ensuring reproducibility and efficiency.
• Implement advanced resource scheduling and orchestration (Slurm, Kubernetes, SUNK) optimized for mixed HPC and AI workflows.
• Support researchers with job optimization, GPU utilization best practices, and performance tuning for AI and HPC applications.
• Evaluate, deploy, and maintain AI/ML software stacks (e.g., PyTorch, TensorFlow, Hugging Face, RAPIDS) and HPC toolchains.
• Ensure robust data ingest, analysis, and management capabilities for AI and HPC workloads, including integration with parallel file systems and object storage.
• Collaborate with diverse science teams to translate research requirements into hardware/software solutions, from experimental design through publication.
• Promote best practices for AI model training, validation, and deployment in shared computing environments.
• Foster a culture of shared learning by running internal workshops on HPC-AI tooling (e.g., VS Code remote dev, containerization, MLOps workflows).

Essential Qualifications

• Bachelor’s or advanced degree in Computer Science, AI/ML, Data Science, Systems Engineering, or related field.
• 10+ years building and managing HPC infrastructure, with significant experience integrating AI/ML workloads.
• Proven track record architecting environments for large-scale GPU AI training and inference in hybrid on-prem/cloud environments.
• Deep expertise with HPC scheduling (Slurm), container orchestration (Kubernetes), and cloud GPU services.
• Strong hands-on experience with AI frameworks (PyTorch, TensorFlow, JAX) and distributed training strategies (Horovod, DeepSpeed, Ray).
• Knowledge of MLOps best practices, including CI/CD for ML, model registry, experiment tracking, and performance monitoring.
• Exceptional ability to collaborate with multidisciplinary teams and communicate complex technical concepts clearly.
• Demonstrated leadership in guiding infrastructure teams, influencing organizational strategy, and fostering adoption of new technologies.

Preferred Qualifications

• Advanced degree in Computer Science, AI/ML, Data Science, Systems Engineering, or related field.
• Experience with advanced Linux systems administration, HPC networking (Infiniband, Ethernet), and storage systems administration (VAST Lustre, Weka and ZFS).
• Cloud platform expertise (Coreweave, AWS, GCP) including GPU provisioning, storage, and networking for AI workloads.
• Proficiency in automation tools (Terraform, Ansible, Puppet), containerization (Docker, Singularity), and orchestration frameworks.
• Strong experience debugging and troubleshooting hardware across the stack (network, GPU, compute and storage systems).
• Strong scripting/programming skills (Python, Bash) and familiarity with version control (Git).
• Experience integrating AI LLMs, AI coding assistants, and custom model development into HPC workflows.

Compensation

The San Francisco, CA base pay range for a new hire in this role is for a Staff HPC Engineer 214,000–$268,000 and for a Senior Staff HPC Engineer $241,000–$300,000. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. This position may be eligible to participate in our discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with our total rewards philosophy and may vary by role.

Benefits

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice.
• Funding for select family-forming benefits.
• Relocation support for employees who need assistance moving.