Biohub

Title: Staff HPC Engineer Location: San Francisco, CA (Hybrid) Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere. The Team 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. We own the design, operation, and reliability of hybrid GPU AI clusters that power frontier AI biology research: protein language models, genomic foundation models, and scientific reasoning systems built to be shared. Our infrastructure supports day-to-day AI researcher workflows. The team works at the intersection of AI tooling, distributed systems, HPC, and frontier AI, debugging deep AI infrastructure problems and building AI systems critical to the entire AI organization. The Opportunity 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. You will design, implement, and optimize a unified HPC-AI ecosystem blending on-prem Slurm-managed clusters, cloud GPU resources, and containerized environments. This hybrid environment will power everything from traditional HPC workloads to large AI training jobs, generative model development, real-time inference, and data-intensive pipelines. The successful candidate will be a thought leader in HPC infrastructure , capable of partnering with scientists, computational biologists, and software engineers to translate complex research needs into high-impact computing solutions. You will also foster adoption of emerging AI tools, and ensure our systems can scale to meet the demands of next-generation biomedical research. What You'll Do HPC Engineering - 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. Operational Excellence - 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. Collaboration & Enablement - Work 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). What You'll Bring Essential - 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. Technical - 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. Better Together As we grow, we’re excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process. Benefits for the Whole You We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. - 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 If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role. #LI-Hybrid

Set technical vision for data representations and tokenization strategies, develop unified training frameworks for biological data, and lead cross-functional initiatives to align technical execution with scientific goals, mentoring senior contributors.

Title: Staff Data Scientist, Imaging Location: Redwood City United States Job Description: Biohub is a 501(c)(3) biomedical research organization building the first large-scale scientific initiative combining frontier AI with frontier biology to solve disease. We build the technology to help scientists around the world use AI-powered biology to study how cells operate, organize, and work as part of systems to understand why disease happens and how to correct it. With our compute capacity, AI research and engineering, and state-of-the-art technology for measuring, imaging, and programming biology, we are enabling scientists worldwide to use AI-powered biology to advance our understanding of human health. The Team Our AI research team sits at the heart of our mission to unlock new dimensions of biological understanding. You will leverage state-of-the-art AI to accelerate discovery and drive transformative insights in biology-developing novel AI models purpose-built for biological research, engineering robust systems that enable breakthrough science at unprecedented scale, and translating these advances into practical tools that empower researchers worldwide. Our approach is comprehensive and integrated, bringing together world-class AI model development, exceptional engineering talent, high-quality biological data, powerful computing infrastructure, and strategic partnerships. Success requires excellence across five interconnected pillars: training frontier AI models specifically for biology; building engineering systems that maximize research velocity and efficiency; executing a sophisticated data strategy that fuels AI development; operating a world-class AI compute platform; and creating impactful products that transform AI capabilities into accessible scientific tools. The Opportunity This role is part of the Data team, which focuses on owning the strategy, sourcing and implementation for data supporting AI research and development. Our goal is to maximize the speed, agility, and capability of biological AI research by connecting public data resources and Biohub's experimental platforms to AI systems. The data that trains biological frontier models comes in dozens of modalities-sequences, images, spatial coordinates, time series, molecular structures, metadata, preprints and published papers-each with its own noise characteristics, biases, and information content. The question of how to represent this data for learning is one of the most important open problems in biological AI. You will operate with broad scope and high autonomy, influencing roadmap decisions across teams while mentoring senior individual contributors. Success in this role means scaling data systems that are not only large, but adaptive, interpretable, and scientifically grounded, accelerating progress toward robust biological frontier models and ultimately advancing human health. We're looking for data scientists who can work at this frontier: people who understand biological measurement deeply, think creatively about data representations and tokenization strategies, and can translate that thinking into novel training architectures. You'll work directly with experimental and computational scientists, data scientists and AI researchers to define what the models see and how they see it, and data engineers to make this work at scale. This is a role for someone who wants to invent the methods that make biological frontier models possible. What You'll Do - Design data representations and tokenization strategies for imaging data that enable novel model architectures - Coordinate Experimental, Data Science, Data Engineering and AI Research teams to translate biological structure into learnable representations-defining priorities and appropriate structures for metadata and data that information models can access and consume - Work across those teams to guide data acquisition priorities, define quality criteria, and assess external datasets from a representation perspective - Develop and validate approaches for combining heterogeneous data modalities into unified training frameworks, designing for robustness to noise, bias, and batch effects - Evaluate how representation choices impact model performance, identifying which biological signals are captured or lost and iterating to improve What You'll Bring - PhD in computational biology, bioinformatics, or a quantitative biological field - Experience with tokenization strategies for non-text data (images, sequences, graphs, time series) - Track record of novel methodological contributions (publications, open-source tools, or production systems) - Familiarity with biological foundation models (ESM, scGPT, or similar) - Deep understanding of imaging data, their underlying data characteristics, and how to transform raw data into ai-ready datasets. - Experience designing data representations or feature engineering for machine learning, ideally in scientific or biological contexts - Familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning - Strong computational skills (Python, scientific computing libraries); comfort working with large-scale datasets - Creative, first-principles thinking about how to structure data for learning Compensation The Redwood City, CA base pay range for a new hire in this role is $214,000 - $294,800. 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. Better Together As we grow, we're excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team's manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process. Benefits for the Whole You We're thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. - 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