Senior Engineering Manager - Kubernetes Development Platform

About the role

We are looking for a Senior Engineering Manager to lead the design, development, and scaling of a next-generation Kubernetes platform powering enterprise environments. This platform will serve as the foundation for AI/ML workloads, GPU infrastructure, and enterprise applications, delivering hyperscaler-like capabilities in on-prem and hybrid deployments.

Responsibilities

• Lead the development of a scalable, enterprise-grade Kubernetes platform that powers AI and mission-critical workloads in on-prem and hybrid environments.
• Own end-to-end delivery of key platform capabilities, including cluster lifecycle, fleet management, and multi-tenancy.
• Drive the design of large-scale distributed systems, evolving toward global control planes and cell-based architectures.
• Lead a team of engineers to build AI-native infrastructure, including GPU-aware scheduling, resource isolation, and workload orchestration.
• Partner closely with Product and cross-functional teams to translate enterprise and AI use cases into platform capabilities.
• Establish a strong operational excellence culture, including SLOs, reliability engineering, and production readiness.
• Simplify complex infrastructure into intuitive, consumable platform experiences for enterprise users.
• Play a key role in shaping a platform that brings hyperscaler-like capabilities into enterprise data centers.

Requirements

The work setup for this role operates in a hybrid environment, with a strong preference for local candidates. While specific days for in-office attendance are not strictly defined, there is an expectation for the new hire to participate in office activities for certain interviews and collaborative sessions, fostering effective team interaction.

Qualifications

• Strong Engineering Leadership
• Proven experience leading and scaling high-performing engineering teams
• Ability to drive clarity, ownership, and execution in complex, ambiguous problem spaces
• Deep Technical Expertise
• Strong understanding of distributed systems at scale
• Hands-on familiarity with cloud platforms, infrastructure systems, or PaaS offerings
• Experience building large, meaningful production systems (cloud platforms, infrastructure, or PaaS)
• Kubernetes experience is desirable, but not required—we welcome leaders who are excited to learn Kubernetes deeply and apply strong systems fundamentals to this domain
• Platform & Systems Thinking
• Experience designing multi-tenant platforms with clear abstractions (projects, quotas, policies)
• Familiarity with multi-cluster / fleet management and large-scale system design
• Ability to balance long-term architecture with near-term delivery
• AI / Infrastructure Awareness (Preferred But Not Required)
• Exposure to AI/ML workloads or GPU-based systems is a plus
• Equally, we welcome strong platform engineers who are excited to grow into AI infrastructure—this role offers the opportunity to learn and build in the rapidly evolving space of GPU scheduling, training, and inference systems

SkillsStrong Engineering Leadership

Proven experience leading and scaling high-performing engineering teams

Ability to drive clarity, ownership, and execution in complex, ambiguous problem spaces

Deep Technical Expertise

Strong understanding of distributed systems at scale

Hands-on familiarity with cloud platforms, infrastructure systems, or PaaS offerings

Experience building large, meaningful production systems (cloud platforms, infrastructure, or PaaS)

Kubernetes experience is desirable, but not required—we welcome leaders who are excited to learn Kubernetes deeply and apply strong systems fundamentals to this domain

Platform & Systems Thinking

Experience designing multi-tenant platforms with clear abstractions (projects, quotas, policies)

Familiarity with multi-cluster / fleet management and large-scale system design

Ability to balance long-term architecture with near-term delivery

AI / Infrastructure Awareness (Preferred But Not Required)

Exposure to AI/ML workloads or GPU-based systems is a plus

Equally, we welcome strong platform engineers who are excited to grow into AI infrastructure—this role offers the opportunity to learn and build in the rapidly evolving space of GPU scheduling, training, and inference systems

Benefits

The work setup for this role operates in a hybrid environment, with a strong preference for local candidates. While specific days for in-office attendance are not strictly defined, there is an expectation for the new hire to participate in office activities for certain interviews and collaborative sessions, fostering effective team interaction.

Pay

The pay range for this position at commencement of employment is expected to be between USD $195,200 and USD $391,200 per year. However, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience.

Additional Information

The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave).