Senior AI Compute & Networking Engineer

Remanie AI Incorporated; Palo Alto (CA); On-site; USD 190,000 - 250,000; Full time

Benefits offered by this job

Equity

Medical coverage

Dental coverage

Vision coverage

Flexible PTO

Daily meals

Job summary

Remanie AI Incorporated is seeking a Senior AI Compute & Networking Engineer to lead the design and optimization of GPU compute and networking infrastructure for AI data centers in Palo Alto. You will own reference architectures, evaluate NVIDIA platforms, and coordinate with OEMs, vendors, and software teams to translate requirements into BOMs and procurement workflows.

The role emphasizes systems engineering and infrastructure, not routine network administration, with a base salary range of

Qualifications

• Bachelor's degree or equivalent in a related field or practical experience.
• Significant AI compute or hyperscale data-center experience.
• Design or architecture experience with GPU clusters and high-speed networks.
• Familiarity with InfiniBand, high-speed Ethernet, and AI cluster connectivity.
• Experience evaluating NVIDIA GPU platforms and infrastructure requirements.
• Ability to lead external partners and understand rack-level power and cooling.

Responsibilities

• Own AI compute and networking reference architectures and approved configurations.
• Develop reusable GPU cluster options based on workload and expansion needs.
• Evaluate NVIDIA DGX, HGX, DGX SuperPOD, and other platforms.
• Define training, inference, storage, and management network topologies.
• Standardize rack configurations, layouts, and interfaces with power and cooling.
• Translate compute decisions into electrical, heat, and space planning requirements.
• Optimize for performance, cost, availability, and deployment speed.
• Translate customer requirements into software inputs and configuration logic.
• Define hardware compatibility constraints and cross-system dependencies.
• Support automated compute/network architecture, BOM, and procurement packages.
• Incorporate OEM lead times, supplier availability, and costs into rules.
• Validate configurations against platform requirements and deployment constraints.
• Refresh software logic as technologies evolve and build data-center workflows.

Skills

AI compute

High-performance HPC

Hyperscale data-center

GPU clusters

Network topology

NVIDIA GPU platforms

Education

Bachelor's degree or equivalent

Job description

Department: Strategic Engineering | Reports to: James Trebesch, Head of Strategic Engineering | Employment: Full-Time

Office Location: Palo Alto, California

Proposed Annual Base Salary: $190,000-$250,000

Plus significant equity and comprehensive benefits.

About the Role

We are seeking a Senior AI Compute & Networking Engineer to lead the development and optimization of standardized GPU compute and networking infrastructure for AI data centers. This role owns compute and networking reference architectures, evaluates NVIDIA and other GPU platform requirements, and establishes repeatable cluster, network, and rack configurations that connect directly to physical facility requirements. The engineer will lead technical work with OEMs, vendors, integrators, and external consultants while collaborating with software teams to translate customer requirements into configuration logic, equipment selection, bills of materials, and procurement workflows. This is a systems engineering and infrastructure product role, not routine network administration.

Key Responsibilities

Compute & Networking Architecture

• Own Remanie's AI compute and networking reference architectures, standards, and approved configurations.
• Develop reusable GPU cluster options based on workload, GPU platform, cluster size, networking, and expansion requirements.
• Evaluate NVIDIA DGX, HGX, DGX SuperPOD, rack-scale GPU systems, and emerging reference designs.
• Define training, inference, storage, and management network topologies, including bandwidth, latency, cabling, and connectivity.
• Standardize rack configurations, networking equipment, physical layouts, and interfaces with power and cooling.
• Translate compute decisions into electrical demand, heat rejection, liquid-cooling requirements, space planning, and facility interfaces.
• Optimize for performance, hardware utilization, capital cost, technical compatibility, availability, and deployment speed.

Engineering Software & Procurement Development

• Translate customer GPU, workload, network, and expansion requirements into structured software inputs and configuration logic.
• Define hardware compatibility constraints, architecture-selection criteria, qualified equipment options, and cross-system dependencies.
• Support automated preliminary compute/network architecture, equipment schedules, bills of materials, and procurement packages.
• Incorporate OEM lead times, supplier availability, costs, and substitutions into equipment selection rules.
• Validate software-generated configurations against platform requirements and deployment constraints.
• Help build the requirements-to-configured-and-purchasable-data-center workflow; refresh software logic as technologies evolve.

External Engineering, OEM & Vendor Leadership

• Direct equipment manufacturers, systems integrators, and external technical partners responsible for detailed design and implementation.
• Establish reference specifications, work scopes, technical integration criteria, and review requirements.
• Challenge OEM proposals, cluster configurations, network designs, and vendor assumptions.
• Identify and maintain approved equipment configurations and qualified alternative suppliers.
• Coordinate with facility engineering firms so compute requirements appear correctly in detailed electrical, mechanical, and civil designs.
• Standardize equipment installation, integration, testing, commissioning, and deployment requirements.

Cross-Disciplinary Collaboration

• Work with electrical, mechanical, and civil disciplines to define integrated infrastructure products.
• Support customer technical discovery and evaluate implications of new GPU and networking generations.
• Incorporate deployment, commissioning, and operational feedback into revised reference systems.

Required Qualifications

• Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or related discipline, or equivalent practical experience.
• Significant AI compute, high-performance computing, hyperscale data-center, or large-scale network experience.
• Demonstrated design or architecture experience with GPU clusters, high-performance networks, or large compute environments.
• Strong understanding of InfiniBand, high-speed Ethernet, network topology, and AI cluster connectivity.
• Familiarity with NVIDIA GPU platforms and associated infrastructure requirements.
• Experience evaluating technical architectures, OEM equipment, integration constraints, and system-level tradeoffs.
• Ability to lead external technical partners and understand rack-level power, thermal, and facility interfaces.

Preferred Qualifications

• Direct NVIDIA DGX SuperPOD, HGX, or large-scale AI cluster deployment experience.
• NVIDIA Spectrum-X, Quantum InfiniBand, or comparable high-performance networking systems.
• Standardized compute/network reference design development.
• Hyperscaler, GPU manufacturer, OEM, systems integrator, or AI infrastructure developer experience.
• High-density, liquid-cooled AI compute deployments.
• Infrastructure configurators, automated architecture tools, structured product data, and BOM generation.
• Compute hardware supply chain, supplier qualification, procurement, and deployment/commissioning workflows.

What Success Looks Like

• Validated, reusable compute and networking configurations for customer requirements.
• Software-generated compute/network equipment packages and clear power, cooling, and site interfaces.
• Less custom design and integration work with faster, more reliable procurement and delivery.

Why Join Remanie

This foundational role reports directly to the Head of Strategic Engineering and shapes the compute backbone of Remanie's requirements-to-purchase infrastructure platform. Software coding is not required; translating compute expertise into structured product logic is essential. As the platform evolves, the same engineering and procurement capabilities may also support adjacent markets such as robotics and industrial automation.

Compensation

$190,000-$250,000 base plus significant equity. We also offer medical, dental and vision coverage, flexible PTO, daily meals, and whatever AI tools you need.