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 network training and inference at unprecedented scale, with an accent on global compute strategy, petabyte-scale data federation, and cross-region GPU job execution. Focus on designing highly performant, resilient, and cost-efficient architectures for AI model development lifecycle, enabling rapid model deployment and ensuring platform scalability.

Location: Hybrid in Sunnyvale, California, USA

Company

hirify.global is a leading developer of Embodied AI technology, creating advanced AI software and foundation models for automated driving systems.

What you will do

• Define and evolve global compute architecture for thousands of GPUs across data centers, ensuring optimal throughput and cost efficiency.
• Design petabyte-scale data federation systems for fast, reliable access to high-volume sensor and simulation data across geographies.
• Build foundations to enable large-scale AI workloads to run seamlessly across hybrid and multi-cloud environments.
• Act as a trusted partner to leadership in aligning compute investments and architecture with company strategy.
• Provide technical leadership and mentorship, cultivating operational and engineering excellence across the engineering organization.

Requirements

• 10+ years designing and building large-scale distributed systems, with at least 4 years focused on GPU-based cloud infrastructure.
• Proven experience enabling large-scale AI training, inference, or computer vision workloads in GPU clusters.
• Deep understanding of petabyte-scale data architecture, including storage federation, high-throughput access, and data locality for AI workloads.
• Strong technical leadership with a track record of defining and communicating architectural strategy.
• A natural mentor with a history of developing engineers and influencing technical direction across teams.
• Advanced degree in Computer Science, Electrical Engineering, or a related field—or equivalent industry experience.

Nice to have

• Experience with multi-cloud orchestration, particularly in latency- or cost-sensitive training and inference pipelines.
• Familiarity with systems like Ray, Kubernetes, Airflow, or Flyte, and deep fluency in AI/ML job scheduling, model lifecycle management, and infrastructure-as-code practices.
• Background in supporting safety-critical or real-time inference use cases (e.g., robotics, autonomous vehicles, aerospace).
• Passion for building infrastructure-as-a-product that delivers performance and simplicity to research and product teams alike.

Culture & Benefits

• Operate a hybrid working policy combining time in offices/workshops with working from home.
• Committed to creating a diverse, fair, and respectful culture inclusive of everyone.
• Embrace uncertainty and complex challenges to unlock groundbreaking solutions.
• Value diversity, embrace new perspectives, and foster an inclusive work environment.
• Constantly learning and evolving in pursuit of excellence.