Senior Software Engineer II, AI Workload Orchestration
About the role
Design, build, and operate Kubernetes-native services for AI workload orchestration and scheduling. Own one or more platform components end-to-end, including design, implementation, testing, and on-call support. Improve scheduling latency, cluster utilization, and workload reliability through metrics-driven engineering. Contribute to architectural discussions across services and influence design decisions within the platform. Work closely with adjacent teams (CKS, infrastructure, managed inference) to ensure clean interfaces and integrations. Mentor junior engineers and raise the quality bar for code, design, and operations.
Responsibilities
- Own meaningful components of the platform, driving reliability and performance improvements
- Help scale the system as customer demand and workload complexity continue to grow
Requirements
5–8 years of professional software engineering experience in distributed systems, cloud infrastructure, or platform engineering
Strong experience building production systems in Go (Python or C++ a plus)
Solid understanding of Kubernetes fundamentals, APIs, controllers, and operating services in production
Experience working with scheduling, resource management, or quota-based systems
Proven ability to improve system reliability and performance using data and operational metrics
Comfortable owning services in production and participating in on-call rotations
Preferred Experience
- With Kubernetes-native orchestration frameworks such as Kueue, Volcano, Ray, Kubeflow, or Argo Workflows
- Familiarity with GPU-based workloads, ML training, or inference pipelines
- Knowledge of scheduling concepts such as quota enforcement, pre-emption, and backfilling
- Experience with reliability practices including SLOs, alerting, and incident response
- Knowledge of AI infrastructure, HPC, or large-scale distributed compute environments
Qualifications
5–8 years of professional software engineering experience in distributed systems, cloud infrastructure, or platform engineering
Strong experience building production systems in Go (Python or C++ a plus)
Solid understanding of Kubernetes fundamentals, APIs, controllers, and operating services in production
Experience working with scheduling, resource management, or quota-based systems
Proven ability to improve system reliability and performance using data and operational metrics
Comfortable owning services in production and participating in on-call rotations
Skills
Experience with Kubernetes-native orchestration frameworks such as Kueue, Volcano, Ray, Kubeflow, or Argo Workflows
Familiarity with GPU-based workloads, ML training, or inference pipelines
Knowledge of scheduling concepts such as quota enforcement, pre-emption, and backfilling
Experience with reliability practices including SLOs, alerting, and incident response
Knowledge of AI infrastructure, HPC, or large-scale distributed compute environments
Benefits
The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).
Pay
The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).
Schedule
The schedule for this role is typically full-time.