---
title: 'Senior HPC & GPU Infrastructure Engineer at Sciforium'
canonical: 'https://feeny.ai/job/senior-hpc-gpu-infrastructure-engineer-sciforium-san-francisco-nay03tzkvjn1'
type: 'job'
last_seen: '2026-11-08'
---

# Senior HPC & GPU Infrastructure Engineer at Sciforium

- **Company:** Sciforium
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-06
- **Last confirmed live:** 2026-11-08
- **Apply:** https://jobs.ashbyhq.com/Sciforium/840adeda-38a1-4ca4-b7c9-304f651e2e55

## Job description

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.

## About the role

We are seeking a Senior HPC & GPU Infrastructure Engineer to take full ownership of the health, reliability, and performance of our GPU compute cluster. You will be the primary custodian of our high-density accelerator environment and the linchpin between hardware operations, distributed systems, and machine learning workflows. This role spans everything from hands-on Linux systems engineering and GPU driver bring-up to maintaining the ML software stack (CUDA/ROCm, PyTorch, JAX, vLLM). If you love squeezing every bit of performance out of hardware, enjoy debugging GPUs at scale, and want to build world-class AI infrastructure, this role is for you.

## What you'll do

1. System Health & Reliability (SRE)

- On-Call Response: Act as the primary responder for system outages, GPU failures, node crashes, and cluster-wide incidents. Minimize downtime by resolving issues rapidly.
- Cluster Monitoring: Implement and maintain monitoring for GPU health, thermal behavior, PCIe/NVLink topology issues, memory errors, and overall system load.
- Vendor Liaison: Coordinate with data center staff, hardware vendors, and on-site technicians for repairs, RMA processing, and physical maintenance of the cluster.

1. Linux & Network Administration

- OS Management: Install, patch, and maintain Linux distributions (Ubuntu / CentOS / RHEL). Ensure consistent configuration, kernel tuning, and automation for large node fleets.
- Security & Access Controls: Configure VPNs, iptables/firewalls, SSH hardening, and network routing to secure our computer infrastructure.
- Identity & Storage Management: Manage LDAP/FreeIPA/AD for user identity, and administer distributed file systems such as NFS, GPFS, or Lustre.

1. GPU & ML Stack Engineering

- Deployment & Bring-Up: Lead deployment of new GPU nodes, including BIOS configuration, NUMA tuning, GPU topology validation, and cluster integration.
- Driver & Kernel Management: Build and optimize kernel modules, maintain GPU drivers and runtime stacks for both NVIDIA (CUDA) and AMD (ROCm).
- Software Stack Maintenance: Maintain and optimize ML frameworks and libraries PyTorch, JAX, CUDA toolkit, cuDNN, ROCm, NCCL, and supporting runtime systems.
- Advanced Debugging: Troubleshoot complex interactions involving GPUs, compilers, ML frameworks, and distributed training runtimes (e.g., vLLM compilation failures, CUDA memory leaks, ROCm kernel crashes).

Ideal candidate profile

- 5+ years of experience in HPC, GPU cluster operations, Linux systems engineering, or similar roles.
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
- Strong expertise with NVIDIA (H100/B200) or AMD (MI325x/MI355x) GPUs, including driver and kernel-level debugging.
- Deep understanding of Linux internals, kernel modules, hardware bring-up, and systems performance tuning.
- Experience with network security, including VPNs, iptables/firewalld, SSH, and identity management (LDAP/FreeIPA/AD).
- Proficiency in Bash and Python for scripting, automation, and workflow tooling.
- Familiarity with ML software stacks: CUDA toolkit, cuDNN, NCCL, ROCm, JAX/PyTorch runtime behavior.
- Deep debugging experience with NVLink/NVSwitch fabrics and RDMA networking.

Nice-to-have

- Experience with job schedulers such as Slurm, Kubernetes, or Run:AI.
- Exposure to vLLM, model serving optimizations, or inference systems.
- Hands-on experience with configuration management tools (Ansible, SaltStack, Terraform).
- Previous experience supporting ML research teams in a startup or research-heavy environment.

## Benefits include

- Medical, dental, and vision insurance
- 401k plan
- Daily lunch, snacks, and beverages
- Flexible time off
- Competitive salary and equity

## Equal opportunity

Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

## About Sciforium

## Company Overview
- **One-liner**: Sciforium is building a vertically integrated AI infrastructure platform that owns its hardware (AMD GPUs) to deliver cost-effective, high-performance inference and foundation model training across text, image, video, and audio modalities.
- **Entity Type**: Private (Seed Stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Hassan Akbari

## Core Business
- **Primary Industry**: AI Infrastructure / Generative AI
- **Target Customers**: B2B; AI teams and enterprises that need scalable, multimodal AI inference and model serving without managing their own infrastructure.
- **Mission**: To rebuild AI serving infrastructure from the ground up—owning the hardware and optimizing the entire pipeline end to end—so that any team, regardless of size or budget, can access the best AI capabilities across every modality without compromise.

## Products & Services
- **AI Inference API**: A drop-in replacement for the OpenAI API format, supporting streaming, tool use, structured outputs, and async generation. Runs on Sciforium’s own AMD hardware for lower cost and stronger privacy.
- **Evaluation Platform**: Built-in pipelines to monitor model performance in real time, catch regressions, and benchmark across models.
- **Native Agents Infrastructure**: Serverless platform for running AI agents at scale without managing servers.
- **Model Library**: Access to state-of-the-art open-source models across text, image, video, and audio (e.g., DeepSeek, Wan2, speech models).

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding of $3.9M (Seed round closed June 2024)
- **Notable Investors/Partners**: Backed by AMD and SignalFire
- **Growth Signals**: Headcount grew 160% YoY to 8 employees; 10 active job postings as of mid-2025; 99.98% uptime claimed; N+1 power and N+2 cooling redundancy with liquid-cooled infrastructure.

## Competitive Advantages
- **Vertical Integration**: Owns its own AMD hardware and runs its own data centers, removing intermediaries and markups—leading to lower costs and predictable performance.
- **Multimodal Native**: Built from the ground up to handle text, image, video, and audio in a single API, unlike many competitors that focus on text-only.
- **Privacy & Control**: By running on dedicated infrastructure (not shared servers), customers get stronger data privacy guarantees.
- **High Ambition Culture**: Team includes alumni from Google DeepMind, Microsoft, Amazon, Snowflake, Qualcomm, and Columbia University.

## Strategic Focus
- **Infrastructure Ownership**: Continuing to invest in its own AMD GPU clusters and data center operations to maintain cost and performance advantages.
- **Multimodal Expansion**: Scaling support for all data types (text, image, video, audio) with a single API.
- **Agent Readiness**: Building native support for AI agents at scale.
- **Open-Source Ecosystem**: Supporting the latest open-source models on day one.

## Why Work Here
- **Culture**: Highly independent, self-motivated, and creative environment. Small enough that your work is visible from day one. Principles include relentless quality, outcome ownership, and high ambition.
- **Work Policy**: Hybrid and in-office roles available. Offices in San Francisco (HQ) and Los Altos, California.
- **Team**: Small, high-caliber team with deep experience from Google DeepMind, Snowflake, Amazon, Qualcomm, and other top AI/infra companies.
- **Perks**: Work on hard infrastructure problems that matter, at a company backed by AMD and SignalFire. Opportunity to shape the foundation of AI infrastructure from an early stage.

## Sources
1. [sciforium.com](https://sciforium.com/)
2. [sciforium.com/company](https://sciforium.com/company)
3. [linkedin.com/company/sciforium](https://www.linkedin.com/company/sciforium)
4. [builtin.com/company/sciforium](https://builtin.com/company/sciforium)
5. [jobs.ashbyhq.com/sciforium](https://jobs.ashbyhq.com/sciforium)

## Other roles at Sciforium

- [GPU Kernel Engineer](https://feeny.ai/job/gpu-kernel-engineer-sciforium-san-francisco-4nr0zgdjbayy) — San Francisco, CA
- [Office Manager](https://feeny.ai/job/office-manager-sciforium-san-francisco-fjg685hp597b) — San Francisco, CA
- [Foundation Model Data Engineer](https://feeny.ai/job/foundation-model-data-engineer-sciforium-san-francisco-dy434fxhsq1d) — San Francisco, CA
- [Pre-training Research Engineer](https://feeny.ai/job/pre-training-research-engineer-sciforium-san-francisco-tt9hpgxczr77) — San Francisco, CA
- [Research Engineer - Model Evaluation & MLOps](https://feeny.ai/job/research-engineer-model-evaluation-mlops-sciforium-san-francisco-mggjh9x4f68q) — San Francisco, CA
- [Distributed Training and Inference Engineer](https://feeny.ai/job/distributed-training-and-inference-engineer-sciforium-san-francisco-frsv71vaddjd) — San Francisco, CA
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- [GPU Cluster Engineer, Networking](https://feeny.ai/job/gpu-cluster-engineer-networking-sciforium-san-francisco-6vx533qasfgy) — San Francisco, CA
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