Director, Compute Engineering

ServiceNow•Santa Clara, CA

11d•$221,200 - $387,100

About The Position

The Director, Compute Engineering leads the engineering organization responsible for building and operating ServiceNow’s compute platform. The role evolves the platform into a scalable, efficient, AI-driven compute model spanning private and public cloud, including scheduling, capacity management, and performance engineering. The director provides technical and organizational leadership, develops managers and engineers, owns the compute scheduler roadmap, and defines the compute-at-scale model and its unit economics. The role leads capacity planning across private and public cloud, establishes performance engineering practices, and applies AI and machine learning to forecasting, anomaly detection, and placement optimization. Working across product management, SRE, FinOps, and infrastructure teams, the director sets priorities that connect capacity decisions to cost and customer outcomes, advances engineering standards and operational practices, manages organizational resources, and defines measures of success for the first 12 to 18 months.

Requirements

15+ years of software engineering experience with a Bachelor's degree; or 12 years with a Master's degree; or a PhD with 8 years of experience; or equivalent experience.
5+ years of people management experience, ideally leading software or systems engineering teams in a global 24x7 production environment.
Experience building or operating large-scale schedulers or resource managers, such as Kubernetes, Slurm, or internal equivalents.
Deep expertise in distributed systems, system design, and capacity planning at scale.
Hands-on experience applying ML in production, including models, feature pipelines, or ML platforms; using AI tools alone is not sufficient.
Experience running performance engineering or capacity management across hybrid or multi-cloud estates, including private and public cloud platforms.
Fluency in multiple programming languages, such as Java, Python, or Go, and strong system design judgment.
Experience with CI/CD, DevOps, configuration management at scale, and automation tools.
Deep background in Linux systems architecture and engineering on a global scale.
Working knowledge of infrastructure components, including hardware platforms, operating systems, virtualization, cloud platforms, web applications, and databases.
Financial acumen in cloud and infrastructure spending, including cost modeling and FinOps practices.
Demonstrated experience using AI to improve work processes, decision-making, or problem-solving.
Ability to define problems, collect data, establish facts, and draw valid conclusions.
Strong leadership, communication, and cross-functional skills, with a record of building consensus and driving complex projects to completion.

Nice To Haves

Experience with ML-driven scheduling, forecasting, or capacity optimization.
Experience with release management and managing large-scale server infrastructure.

Responsibilities

Lead the compute engineering organization and provide technical leadership, mentoring, and career development for managers and engineers.
Own the compute scheduler roadmap, including placement, bin-packing, prioritization, preemption, and workload fairness.
Define and drive the compute-at-scale model, including unit economics such as cost per workload, utilization, and capacity headroom.
Lead capacity management for private and public cloud, including demand forecasting, reservations, procurement, and cross-cloud placement.
Establish and scale performance engineering practices, including benchmarking, load and scale testing, regression detection, and SLOs for scheduling latency and throughput.
Apply AI and ML to demand forecasting, anomaly detection, and placement optimization; set the AI/ML roadmap and determine where it outperforms heuristics.
Work with product management, SRE, FinOps, and infrastructure partners to set priorities and connect capacity decisions to cost and customer outcomes.
Drive engineering excellence through architecture reviews, design standards, and modern software and DevOps practices.
Anticipate technical and organizational risks affecting multiple products or platforms and provide structured mitigations.
Assess availability, scalability, and operational risk across compute systems, and lead resulting optimization work.
Manage resources, staffing, and succession planning to build and retain a strong engineering organization.
Share best practices and engineering standards with teams across the organization.
Define success measures for the first 12 to 18 months, such as utilization gains, scheduler latency targets, forecast accuracy, and cost per unit.