Engineering Manager - Managed Kubernetes Services

Senior Engineering Manager

Hungry Humble Honest The Opportunity 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. You will lead a team responsible for building a production-grade, globally scalable Kubernetes platform, including cluster lifecycle, fleet management, multi-tenancy, and deep integration with compute (CPU/GPU), networking, and storage systems.

About the Team We are building a next-generation Kubernetes platform for enterprise AI and infrastructure, designed to bring hyperscaler-grade capabilities into on-prem environments. The team owns the full stackfrom cluster lifecycle and fleet management to multi-tenancy and AI workload orchestration (including GPUs). Our charter is to enable enterprises to run mission-critical and AI workloads at massive scale, with a strong focus on simplicity, reliability, and performance. We operate as a highly collaborative, globally distributed team spanning the US and India, working closely with product, hardware, and field organizations.

You will report to the Director of Engineering, who embodies a leadership style that emphasizes strong managerial expertise, technical involvement, and collaboration with engineers. 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.

Your Role As a Senior Engineering Manager, you will lead the development of a scalable, enterprise-grade Kubernetes platform that powers AI and mission-critical workloads in on-prem and hybrid environments. You will:

• 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

You will play a key role in shaping a platform that brings hyperscaler-like capabilities into enterprise data centers.

What You Will Bring 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 requiredwe 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 infrastructurethis role offers the opportunity to learn and build in the rapidly evolving space of GPU scheduling, training, and inference systems

Execution & Operational Excellence

• Track record of delivering reliable, production-grade systems
• Experience with SLOs, observability, incident management, and lifecycle operations

Collaboration & Influence

• Strong ability to work across product, hardware, and field teams
• Effective executive-level communication and stakeholder management

Work Arrangement Hybrid: This role operates in a hybrid capacity, blending the benefits of remote work with the advantages of in-person collaboration. In locations where our workplace policy applies (i.e. San Jose, Durham, Mexico City, Bangalore, Pune, Hoofddorp, Belgrade, Barcelona, Singapore, Sydney and Tokyo), employees are expected to work onsite a minimum of 3 days per week to foster collaboration, team alignment, and access to in-office resources. Workplace type may vary based on location and team requirements. Please speak with your recruiter for details. Additional team-specific guidance and norms will be provided by your manager.

The pay range for this position at commencement of employment is expected to be between USD $201,000 and USD $402,000 per year. However, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. 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), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employees will be in an "at-will position" and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

Our application deadline is 40 days from the date of posting. In good faith, the posting may be removed prior to this date if the position is filled or extended in good faith.