Network Simulation Engineer (AI)

Location: Saratoga, California, United States

Company

hirify.global is a Silicon Valley hardware startup developing infrastructure solutions for AI data centers across silicon, packaging, software, and systems.

What you will do

• Simulate communication patterns for distributed AI workloads, including LLMs and DLRMs, across diverse data center network topologies.
• Analyze workload performance and scalability to support architecture decisions throughout the product development lifecycle.
• Evaluate customer AI workloads and recommend topology designs, protocol tuning, and system architecture improvements.
• Provide simulation-based insights to ASIC and architecture teams to optimize chip designs for AI traffic flows.
• Customize, tune, enhance, and operate simulation frameworks such as NS-3 and ASTRA-sim in collaboration with tool providers.
• Prepare reports, technical documentation, and presentations for technical and non-technical stakeholders.

Requirements

• MSc or PhD in Computer Science, Electrical Engineering, or a related field specializing in AI/ML communications, or equivalent hands-on experience.
• Strong experience with network simulation tools such as NS-3, OMNeT++, or custom-built simulators.
• Familiarity with distributed training frameworks, collective communication libraries, and GPU programming with CUDA or ROCm.
• Deep understanding of frontier model architectures, parallelism, Ethernet, InfiniBand, and high-performance data center networking.
• Understanding of compute, memory, and interconnect bottlenecks in large-scale AI training and inference clusters.
• Strong C++ and Python programming skills, plus clear written and verbal communication.

Nice to have

• 2+ years of relevant experience.
• Exceptional early-career candidates may also be considered.

Culture & Benefits

• Work on next-generation AI infrastructure and high-performance data center networking.
• Collaborate with customers, ASIC designers, simulation tool providers, and experienced engineering leaders.
• Contribute directly to products designed to improve AI performance, GPU utilization, capital efficiency, power consumption, and reliability.
• Starting base salary is determined by skills, experience, qualifications, work location, market trends, and comparable employee compensation.