Director, Software Engineering

ServiceNow•Santa Clara, CA

About The Position

The Director, Software Engineering will lead the engineering organization responsible for building and operating ServiceNow’s compute platform. The role will evolve the platform into a scalable, efficient, AI-driven compute model across private and public clouds, covering scheduling, capacity management, and performance engineering. The director will lead managers and engineers, own the compute scheduler roadmap, define the compute-at-scale model and its unit economics, and manage capacity across cloud environments. They will establish performance engineering practices and apply AI and machine learning to forecasting, anomaly detection, and placement optimization. The role partners with product management, SRE, FinOps, and infrastructure teams, while driving engineering standards, operational risk management, resource planning, and measurable outcomes over the first 12 to 18 months.

Requirements

Experience leveraging or critically evaluating how to integrate AI into work processes, decision-making, or problem-solving.
15 or more 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 or more 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 related to cloud and infrastructure spend, 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 completing complex projects.

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 cost per workload, utilization, and capacity headroom.
Lead capacity management across private and public cloud, including demand forecasting, reservations, procurement, and cross-cloud placement.
Establish and scale performance engineering practices for benchmarking, load and scale testing, regression detection, and scheduling latency and throughput SLOs.
Apply AI and ML to demand forecasting, anomaly detection, and placement optimization; set the AI/ML roadmap and determine where it outperforms heuristics.
Partner with product management, SRE, FinOps, and infrastructure teams 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 subsequent optimization work.
Manage resources, staffing, and succession planning to build and retain a high-performing engineering organization.
Share best practices and engineering standards with teams across the organization.
Define success measures for the first 12 to 18 months, including utilization gains, scheduler latency targets, forecast accuracy, and cost per unit.