Principal Engineer, Data & Compute (AI Infrastructure)

TL;DR

Principal Engineer, Data & Compute (AI Infrastructure): Designing and guiding the evolution of foundational compute and storage systems for end-to-end neural networks with an accent on GPU orchestration and petabyte-scale data federation. Focus on building cross-region GPU job execution and scaling infrastructure to exabyte-scale to accelerate AI research and model deployment.

Location: Must be based in Sunnyvale, CA (Hybrid)

Salary: $370,300 – $418,200 plus a competitive equity package

Company

hirify.global is a leading developer of Embodied AI technology creating mapless, hardware-agnostic AI products for automated driving.

What you will do

• Define and evolve global compute strategy for orchestrating training and inference across thousands of GPUs and multiple data centers.
• Design petabyte-scale data federation systems for high-volume sensor and simulation data to prepare the company for exabyte-scale.
• Build the foundations for large-scale AI workloads to run seamlessly across hybrid and multi-cloud environments.
• Align compute investments and architecture with company strategy, growth plans, and performance goals.
• Uplift the engineering organization through architectural coaching, technical deep dives, and mentorship.

Requirements

• 10+ years of experience designing and building large-scale distributed systems.
• 4+ years of experience focused on GPU-based cloud infrastructure.
• Proven track record of enabling large-scale AI training, inference, or computer vision workloads in GPU clusters.
• Deep understanding of petabyte-scale data architecture, including storage federation and high-throughput access.
• Strong technical leadership skills with a history of defining architectural strategy and developing engineers.
• Advanced degree in Computer Science, Electrical Engineering, or equivalent industry experience.
• Must be based in Sunnyvale, CA (Hybrid).

Nice to have

• Experience with multi-cloud orchestration for latency- or cost-sensitive pipelines.
• Fluency in AI/ML job scheduling and tools such as Ray, Kubernetes, Airflow, or Flyte.
• Experience with infrastructure-as-code practices.
• Background in supporting safety-critical or real-time inference for robotics, autonomous vehicles, or aerospace.

Culture & Benefits

• Hybrid working policy combining office/workshop time with working from home.
• Inclusive work environment that values diversity and new perspectives.
• Competitive equity package.
• Opportunity to tackle unprecedented scale in data infrastructure and compute orchestration in a fast-paced environment.