Member of Technical Staff - GPU Infrastructure

Solutions Architect - GPU Infrastructure · Senior · Full Time

San FranciscoUSD 180k – 250k5mo ago
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Role

What you'll do.

Prime Intellect is seeking a highly skilled Member of Technical Staff to design and deploy cutting-edge GPU infrastructure for AI and machine learning workloads. The role focuses on creating robust, scalable compute solutions that enable advanced AI model training and deployment across research, startup, and enterprise environments.

Responsibilities

  • Customer Architecture Design: Partner with clients to design optimal GPU cluster architectures, create technical proposals for clusters ranging from 100 to 10,000+ GPUs, and develop deployment strategies for LLM training, inference, and HPC workloads.
  • Infrastructure Deployment: Deploy and configure orchestration systems like SLURM and Kubernetes, implement high-performance networking, optimize GPU utilization, and configure parallel filesystems for maximum performance.
  • Production Operations: Serve as the primary technical escalation point for customer infrastructure issues, diagnose complex problems across the full technology stack, implement monitoring systems, and provide 24/7 on-call support for critical deployments.

Qualifications

What we look for.

Technical

  • GPU Infrastructure Expertise

    Minimum 3+ years of hands-on experience with GPU clusters and HPC environments

  • Orchestration Systems

    Deep expertise with SLURM and Kubernetes in production GPU settings

  • Networking Knowledge

    Proven experience with InfiniBand configuration and troubleshooting

Education

  • Computer Science

    Bachelor's degree in Computer Science, Engineering, or related technical field preferred

Experience

  • Systems Programming

    Demonstrated proficiency in Python, Bash, and systems-level programming

  • Infrastructure Automation

    Experience with infrastructure automation tools such as Ansible and Terraform

Skills

Required

  • GPU Architecture

    Strong understanding of NVIDIA GPU architecture, CUDA ecosystem, and driver stack

  • Infrastructure Automation

    Proficiency with Ansible, Terraform, and other infrastructure automation tools

  • Programming Languages

    Advanced skills in Python, Bash, and systems programming

Preferred

  • Large-Scale Deployment

    Nice to have

    Experience with 1000+ GPU deployments

  • Certifications

    Nice to have

    NVIDIA DGX, HGX, or SuperPOD certification

  • Distributed Training

    Nice to have

    Knowledge of distributed training frameworks like PyTorch FSDP, DeepSpeed, Megatron-LM

Tech stack

Languages

PythonBash

Frameworks

KubernetesSLURM

Databases

Not Specified

Tools

AnsibleTerraformDocker

Other

CUDAInfiniBand

Compensation

Pay and benefits.

Base·USD 180,000 – 250,000

Equity·Stock options

Benefits

  • Equity Compensation

    Stock options in an early-stage AI infrastructure company with significant funding

  • Cutting-Edge Technology

    Work on pioneering AI infrastructure with potential for significant industry impact

  • Professional Growth

    Direct collaboration with world-class engineering team and exposure to frontier AI technologies

Process

Interview steps.

  1. 01

    Initial Screening

    Resume review and preliminary phone/video interview to assess technical background and experience

  2. 02

    Technical Interview

    In-depth technical discussion focusing on GPU infrastructure, HPC environments, and systems architecture

  3. 03

    Systems Design Challenge

    Practical assessment involving design of GPU cluster architecture and solution to complex infrastructure problem

  4. 04

    Final Interview

    Meeting with technical leadership to discuss cultural fit, career growth, and alignment with company mission

Full posting

Original listing.

Building Open Superintelligence Infrastructure

Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full rl post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.

As our Solutions Architect for GPU Infrastructure, you'll be the technical expert who transforms customer requirements into production-ready systems capable of training the world's most advanced AI models.

We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.

Core Technical Responsibilities

This customer-facing role combines deep technical expertise with hands-on implementation. You'll be instrumental in:

Customer Architecture & Design

  • Partner with clients to understand workload requirements and design optimal GPU cluster architectures

  • Create technical proposals and capacity planning for clusters ranging from 100 to 10,000+ GPUs

  • Develop deployment strategies for LLM training, inference, and HPC workloads

  • Present architectural recommendations to technical and executive stakeholders

Infrastructure Deployment & Optimization

  • Deploy and configure orchestration systems including SLURM and Kubernetes for distributed workloads

  • Implement high-performance networking with InfiniBand, RoCE, and NVLink interconnects

  • Optimize GPU utilization, memory management, and inter-node communication

  • Configure parallel filesystems (Lustre, BeeGFS, GPFS) for optimal I/O performance

  • Tune system performance from kernel parameters to CUDA configurations

Production Operations & Support

  • Serve as primary technical escalation point for customer infrastructure issues

  • Diagnose and resolve complex problems across the full stack - hardware, drivers, networking, and software

  • Implement monitoring, alerting, and automated remediation systems

  • Provide 24/7 on-call support for critical customer deployments

  • Create runbooks and documentation for customer operations teams

Technical Requirements

Required Experience

  • 3+ years hands-on experience with GPU clusters and HPC environments

  • Deep expertise with SLURM and Kubernetes in production GPU settings

  • Proven experience with InfiniBand configuration and troubleshooting

  • Strong understanding of NVIDIA GPU architecture, CUDA ecosystem, and driver stack

  • Experience with infrastructure automation tools (Ansible, Terraform)

  • Proficiency in Python, Bash, and systems programming

  • Track record of customer-facing technical leadership

Infrastructure Skills

  • NVIDIA driver installation and troubleshooting (CUDA, Fabric Manager, DCGM)

  • Container runtime configuration for GPUs (Docker, Containerd, Enroot)

  • Linux kernel tuning and performance optimization

  • Network topology design for AI workloads

  • Power and cooling requirements for high-density GPU deployments

Nice to Have

  • Experience with 1000+ GPU deployments

  • NVIDIA DGX, HGX, or SuperPOD certification

  • Distributed training frameworks (PyTorch FSDP, DeepSpeed, Megatron-LM)

  • ML framework optimization and profiling

  • Experience with AMD MI300 or Intel Gaudi accelerators

  • Contributions to open-source HPC/AI infrastructure projects

Growth Opportunity

You'll work directly with customers pushing the boundaries of AI, from startups training foundation models to enterprises deploying massive inference infrastructure. You'll collaborate with our world-class engineering team while having direct impact on systems powering the next generation of AI breakthroughs.

We value expertise and customer obsession - if you're passionate about building reliable, high-performance GPU infrastructure and have a track record of successful large-scale deployments, we want to talk to you.

Apply now and join us in our mission to democratize access to planetary scale computing.

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