Senior Manager, Core Infrastructure Engineering

Responsibilities

• Own and build solutions to scale and optimize AI compute infrastructure components like GPU control plane and GPU data plane with the goal to optimize customer experience and customer workload performance on our AI infrastructure.
• Set and communicate individual expectations and team goals such that they align with the broader organization goals.
• Model and coach team members and drive modern software engineering practices like leveraging data/telemetry to make decisions, well‑defined interfaces across components, design reviews, coding standards, code reviews, and comprehensive coverage from unit test, integration test and active production monitoring.
• Prioritize team’s work with focus on customer issues and requirements.
• Ensure that team solutions are well‑defined and modularized, secure, reliable, diagnosable, actively monitored, compliant and reusable.
• Create roadmap, define SMART goals, and track team progress against‑ committed OKRs.

• 10+ years’ experience in software development with programming languages including, but not limited to, C, C++, C#, Java, Go, Rust.
• 5+ years’ experience in people management or leadership role while working on cross‑functional projects.
• 5+ years’ experience designing and developing large‑scale distributed systems, services, and infrastructure.
• BS (or equivalent experience) in Computer Science, Engineering, or related field.
• Strong communication, collaboration, and project management skills.
• Ability to adapt to a fast‑paced, dynamic environment and manage multiple tasks and priorities effectively.

• Experience managing cloud infrastructure with hundreds of thousands of servers.
• Experience with containerization technologies such as Docker and Kubernetes.
• Experience scheduling high‑performance workloads on Kubernetes or Slurm.

• Manages the development and implementation of scalable distributed systems and components across multiple teams, including the effective use of distributed state management tools.
• Oversees code and/or system optimization efforts for large‑scale data processing and high‑throughput requirements within and across teams to support hyper‑scale systems.
• Guides teams to define scalability requirements for owned components and ensures design and implementation requirements are met.
• Manages the use of data plane platforms to effectively handle large‑scale data retrieval, storage, and processing.
• Ensures team accurately designs performance and load testing.

• Manages the strategy for building fault‑tolerant components and systems capable of withstanding in‑service updates by guiding the implementation of redundancy, replication, and automatic failover mechanisms.
• Develops design strategies for systems to effectively handle service disruptions (e.g., network partitions) by prioritizing consistency, availability, or partition tolerance.
• Leads implementation and optimization initiatives across teams for approaches to handle network unreliability, including load‑shedding, throttling, and rate‑limiting.
• Guides teams to design components and systems that are durable and adhere to service level objectives (SLOs), setting expectations for availability and durability of other computing services within the department.

• Provides oversight in defining key performance indicators (KPIs) and telemetry to identify gaps or issues in running systems.
• Oversees the building and customization of moderately complex dashboards, telemetry systems, and alerting mechanisms to proactively monitor components and system health.

• Oversees the design and implementation of functional and correctness requirements for feature sets and/or systems in new or existing systems.
• Guides teams to design complex test scenarios (e.g., fault‑injection, brown‑out) to evaluate system correctness.
• Directs implementation strategies for data replication and synchronization techniques to maintain data integrity and availability.

• Guides teams to be proactive when diagnosing, debugging,…