Senior Staff Software Engineer – Compute Engineering

ServiceNow•Santa Clara, CA

1d•$190,900 - $334,100

About The Position

ServiceNow is hiring across multiple engineering levels for its Compute Engineering organization, which builds scheduling, capacity, and performance systems that run the platform across private and public cloud. Final level alignment is based on experience, technical expertise, demonstrated impact, and interview performance. The role designs and evolves compute scheduling and resource-management systems, develops capacity models and forecasts, and establishes performance and scale testing. It applies AI and machine learning where these methods outperform simpler approaches, and develops declarative, software-defined infrastructure that is specified, documented, and testable. The engineer also researches tools and methods to improve compute efficiency and reliability, collaborates across engineering and operations teams, guides complex technical decisions, owns production reliability, and mentors technical leaders. At the Senior Staff level, the engineer leads design and delivery of a major compute-platform component, drives quality within a team, 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.

Requirements

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 machine learning in production, including models, feature pipelines, or ML platforms; using AI tools alone is not sufficient.
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.
Experience integrating AI into work processes, decision-making, or problem-solving.
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 new skills and deliver improvements for internal and external customers.
Integrity, honesty, and commitment to ServiceNow's values.

Nice To Haves

Experience with machine-learning-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 placement, prioritization, preemption, and workload fairness.
Build capacity models and forecasts across private and public clouds 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 machine learning to forecasting, anomaly detection, and placement optimization when they outperform simpler approaches.
Write and maintain declarative, software-defined infrastructure at scale, with 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 the design and delivery of a major compute-platform component, drive team quality, 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.