Principal AI Infrastructure Engineer
Role Summary
You will join a high-performance, mission-driven interdisciplinary team that spans data science, software engineering, platform architecture, cloud infrastructure, and security expertise.
Key Responsibilities
Help define and evolve the organizational vision for AI developer experience, identifying gaps and opportunities across the full AI development lifecycle and across all deployment environments.
Lead cross-team initiatives that systematically improve how AI builders experience infrastructure—from experimentation through production, creating measurable improvements in developer productivity and satisfaction.
Work strategically with data science teams, and research groups to understand emerging AI use cases and infrastructure needs at the frontier of the organization's work.
Establish org-wide patterns, standards, and best practices for AI infrastructure that are adopted across multiple business units and geographies.
Design and advocate for developer-first abstractions and platforms that absorb infrastructure complexity while enabling advanced customization for specialized use cases.
Help evolve governance frameworks—including model versioning, experiment tracking, deployment workflows, and compliance standards—that scale with organizational growth without stifling innovation.
Shape the organizational approach to infrastructure cost, performance, and reliability trade-offs, ensuring alignment with business objectives across teams.
Mentor and develop engineers across the organization, creating a culture of architectural excellence and infrastructure craftsmanship.
Anticipate infrastructure, architectural, and organizational risks—from evolving workload patterns to regulatory changes to emerging security threats—and implement durable solutions adopted org-wide.
Lead by example in creating reusable Infrastructure-as-Code frameworks, architectural patterns, and tooling that amplify team productivity and reduce toil across the organization.
Required Skills & Experience
Recognized expert in AI infrastructure with deep knowledge of on-premises, hybrid, and cloud-native architectures, with demonstrated influence and impact across organizations or business units.
Proven track record of architecting infrastructure systems that serve multiple, sometimes competing, organizational needs while maintaining coherence and simplicity.
Expert communication and strategic thinking skills—ability to translate technical architecture into research and product impact.
Expert-level proficiency with Kubernetes, distributed compute frameworks (Ray, Spark, or equivalent), and the ability to define org-wide orchestration and scheduling strategies.
Mastery of Infrastructure-as-Code and GitOps frameworks, with demonstrated ability to design reusable, multi-team infrastructure patterns and platforms.
Deep expertise in GPU and accelerator resource management, cost optimization, and performance tuning across diverse workload types and hardware configurations.
Expert knowledge of cloud platforms (AWS, Azure, or equivalent) and proven ability to architect multi-cloud or hybrid strategies that balance flexibility, cost, and operational complexity.
Strong background in distributed systems design, including handling scale, reliability, consistency, and failure modes across heterogeneous infrastructure.
Demonstrated ability to lead large, cross-team initiatives from conception through execution, influencing complex decision-making and shaping long-range technical directions.
Strong mentoring orientation with demonstrated success developing leaders and upskilling teams across the organization in infrastructure and platform topics.
Recognized ability to drive innovation, anticipate organizational needs, and architect durable solutions that scale with the business.
Preferred Skills
Deep expertise in building or scaling AI infrastructure for robotics, autonomous systems, or industrial perception at enterprise scale, with demonstrated patterns and reusable frameworks.
Expert-level knowledge of ROS/ROS2 ecosystems and the infrastructure challenges of deploying and managing ML models across diverse robotic platforms and environments.
Strategic experience with edge AI deployment and the architectural tradeoffs between centralized cloud inference, edge inference, and hybrid models in physical systems.
Background designing ML infrastructure that supports rapid adaptation, few-shot learning, and task transfer in physical systems, enabling scalable deployment across heterogeneous environments.
Deep understanding of heterogeneous compute architectures (CPUs, GPUs, TPUs, NPUs, FPGAs) and experience optimizing inference pipelines for specialized hardware.
Experience with real-time operating systems and the infrastructure requirements of hard real-time, safety-critical AI systems.
Strategic familiarity with manufacturing, autonomous vehicles, or healthcare domains and the business and technical requirements that shape AI infrastructure in those industries.
Demonstrated ability to influence robotics, manufacturing, or autonomous systems teams and shape architectural decisions that bridge domain expertise with modern AI/ML capabilities.
Track record of translating specific domain engagements into generalizable, org-wide AI infrastructure capabilities and standards.