Principal Staff Engineer – AI Infrastructure - AI​/ML Leader

Principal Staff Engineer - AI Infrastructure About The Role

We are seeking a Principal Staff Engineer to lead the architecture and development of our next‑generation AI infrastructure. This role sits at the intersection of large‑scale distributed systems and cutting‑edge machine learning, powering the platforms that enable researchers and engineers to build, train, and deploy AI models at global scale.

As a senior technical leader, you will define architectural strategy, influence cross‑organizational initiatives, and guide the design of highly reliable, efficient, and scalable systems. You’ll balance deep technical execution with strategic vision—mentoring senior engineers, collaborating with AI researchers, and ensuring our infrastructure accelerates innovation while maintaining world‑class reliability.

• Design & Scale AI Infrastructure:
  Architect and build distributed training, inference, and data pipelines that support large‑scale AI workloads across GPUs and heterogeneous environments.
• Lead Cloud‑Native Innovation:
  Drive adoption of Kubernetes, Docker, and modern orchestration frameworks to optimize model deployment, resource allocation, and cluster utilization.
• Optimize Performance at Scale:
  Develop high‑throughput, low‑latency services and memory‑efficient systems to support petabyte‑scale data and massive model sizes.
• Advance Observability & Reliability:
  Implement monitoring, tracing, and fault‑tolerance strategies to ensure resilient AI systems in production.
• Collaborate with Research & Product:
  Partner with ML scientists, product engineers, and platform teams to design infrastructure that accelerates experimentation and model iteration.
• Mentor & Inspire:
  Support the technical growth of senior engineers, fostering a culture of excellence, innovation, and ownership.
• Shape Technical Strategy:
  Define long‑term roadmaps for AI infrastructure, balancing near‑term delivery with foundational investments in scalability, efficiency, and reliability.

• Extensive
  
  Experience:
  
  10+ years in distributed systems, large‑scale infrastructure, or platform engineering, with experience supporting AI/ML workloads strongly preferred.
• Programming Mastery:
  Deep expertise in Java, Python, or C++, with proven ability to build performant and reliable systems.
• AI/ML Infrastructure Knowledge:
  Familiarity with ML frameworks (Tensor Flow, PyTorch, JAX), distributed training strategies, GPU scheduling, and data pipeline optimization.
• Modern Infrastructure
  
  Skills:
  
  Hands‑on experience with Kubernetes, Docker, CI/CD pipelines, cloud platforms (AWS/GCP/Azure), and observability tools (Prometheus, Grafana, Datadog).
• Systems Design Expertise:
  Strong foundation in algorithms, concurrency, and systems architecture for high‑scale, fault‑tolerant environments.
• Leadership & Influence:
  Demonstrated success driving cross‑functional initiatives, mentoring senior engineers, and setting engineering‑wide standards.
• Product Mindset:
  Ability to balance technical rigor with usability and speed, ensuring infrastructure empowers rapid iteration and impactful outcomes.