Senior Staff Software Engineer – Compute Engineering

ServiceNow•Santa Clara, CA

About The Position

ServiceNow is hiring across multiple engineering levels for its Compute Engineering organization, which builds scheduling, capacity, and performance systems for its platform across private and public clouds. Final level alignment depends on experience, technical expertise, demonstrated impact, and interview performance. At the Senior Staff level, the engineer leads design and delivery of a major compute-platform component, drives team quality, and mentors engineers. The posting also describes Principal-level expectations: setting technical direction across multiple teams or a major compute area, shaping architecture, mentoring senior engineers and architects, and influencing decisions across geographies. The role focuses on designing and evolving compute scheduling and resource-management systems, building capacity models and forecasts, establishing performance and scale testing, and applying AI and ML where they outperform simpler approaches. It also includes developing declarative software-defined infrastructure, evaluating tools and methods, partnering with related engineering and business teams, guiding technical decisions, owning production reliability and incident response, and mentoring technical leaders.

Requirements

Experience leveraging or critically evaluating how to integrate AI into work processes, decision-making, or problem-solving; AI-tool use alone is insufficient.
12+ years of related software engineering, cloud, or infrastructure automation experience with a bachelor's degree; or 8+ years with a master's degree; or a PhD with 5+ years of experience; or equivalent experience.
Deep experience designing multi-tenant, cloud-native SaaS at enterprise scale, including distributed systems, large-scale data systems, APIs, and microservices.
Experience designing or operating large-scale schedulers or resource managers, such as Kubernetes, Slurm, or internal equivalents.
Strong programming skills in Go, Python, or Java, with experience in related languages.
Experience with capacity planning, performance engineering, or load testing at scale.
Hands-on experience applying ML in production, including models, feature pipelines, or ML platforms.
Experience with infrastructure as code and configuration management, such as Terraform and Ansible.
Comfort with virtualization and cloud infrastructure across private and public clouds, such as Azure, AWS, or GCP.
Working understanding of IP networking, block and file storage, and load balancers.
Ability to work independently across interconnected systems and investigate root causes without waiting for direction.
Ability to influence without direct authority across multiple teams and geographies, including the US, EU, and India.
Clear communication with senior leaders, customers, and engineers, including the ability to articulate a compelling technical vision.
A learning mindset and motivation to build skills and improve outcomes for internal and external customers.
Integrity, honesty, and commitment to ServiceNow's values.

Nice To Haves

Experience with ML-driven scheduling, forecasting, or capacity optimization.
Working knowledge of internet protocols such as DNS, NTP, and HTTP.
Experience with release management and large-scale server infrastructure.

Responsibilities

Design and evolve compute scheduler and resource-management systems, including workload placement, prioritization, preemption, and fairness.
Build capacity models and forecasts across private and public cloud, and translate them into reservation and placement decisions.
Establish performance and scale testing, including benchmarks, load tests, regression detection, and SLOs for scheduling latency and throughput.
Apply AI and ML to forecasting, anomaly detection, and placement optimization where they outperform simpler approaches.
Write and maintain declarative, software-defined infrastructure at scale, supported by build systems, web services, and automation that are specified, documented, and testable.
Research and adopt open-source and commercial tools and methods that improve compute efficiency and reliability.
Partner with networking, orchestration, SRE, and FinOps teams on cross-team designs and cost trade-offs.
Guide complex technical decisions and design reviews; challenge assumptions with data, identify architectural risks early, and balance delivery speed with long-term durability.
Own production reliability and incident response for compute systems, and lead post-incident improvements.
Mentor architects and senior engineers and develop the next layer of technical leadership.
At the Senior Staff level, lead design and delivery of a major compute-platform component, drive quality within a team, and mentor engineers.
At the Principal level, set technical direction across multiple teams or a major compute area, shape architecture, mentor senior engineers and architects, and influence decisions across geographies.