Staff Software Engineer – Compute Engineering

ServiceNow•Santa Clara, CA

About The Position

Join ServiceNow’s Compute Engineering organization, which builds the scheduling, capacity, and performance systems that run its platform across private and public cloud. The role develops and maintains software-defined, declarative infrastructure for compute at scale, including build systems, services, and automation that are specified, documented, and testable. It contributes to compute scheduling and resource-management systems, capacity data tooling, performance testing, and adoption of tools and methods that improve efficiency and reliability. The engineer collaborates with networking, orchestration, and SRE teams on integrations and design; participates in design reviews and on-call rotations; and contributes to post-incident improvements. The role also uses AI tools to accelerate development and analysis and helps identify opportunities for machine learning to improve capacity or placement decisions. At Staff level, the engineer owns a component or feature area end to end, including scoping and design; leads team design discussions; mentors IC3 engineers; identifies risks proactively; and influences architectural decisions to simplify and streamline infrastructure.

Requirements

At least 8 years of related software engineering, cloud, or infrastructure automation experience with a Bachelor's degree; or at least 6 years with a Master's degree; or a PhD with at least 3 years of relevant research or industry experience; or equivalent experience.
Strong programming skills in Python, Go, or Java, with experience in related languages.
Experience building and maintaining Linux-based systems using infrastructure-as-code and configuration-management tools 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.
Working understanding of internet protocols such as DNS, NTP, and HTTP.
Ability to work independently across interconnected systems and investigate root causes without waiting for direction.
Experience leveraging or critically considering how to integrate AI into work processes, decision-making, or problem-solving.
Learning mindset and motivation to build new skills and improve outcomes for internal and external customers.
Excellent oral and written communication skills.
Integrity, honesty, and commitment to ServiceNow's values.
Staff-level experience driving architectural discussions and influencing technical decisions to simplify and streamline infrastructure.
Staff-level experience leading the design and delivery of a component or feature area end to end.
Staff-level experience mentoring engineers and supporting their technical growth.

Nice To Haves

Experience with capacity planning, performance testing, or load testing.
Experience with Kubernetes, Slurm, or other large-scale schedulers or resource managers.
Experience with ML-driven scheduling, forecasting, or capacity optimization.
Experience with release management and large-scale server infrastructure.

Responsibilities

Write and maintain software-defined, declarative infrastructure for compute at scale, including build systems, services, and automation that are specified, documented, and testable.
Build and improve compute scheduler and resource-management components, including placement, prioritization, and preemption.
Build tools for capacity data collection, forecasting, and reporting across private and public cloud.
Write performance and load tests and help detect regressions in scheduling latency and throughput.
Research and adopt open-source and commercial tools and methods to improve compute efficiency and reliability.
Collaborate with networking, orchestration, and SRE teams on integration and design.
Participate in design reviews and on-call rotations, and contribute to post-incident improvements.
Use AI tools to accelerate development and analysis and help identify opportunities for ML to improve capacity or placement decisions.
Own a component or feature area end to end, including scoping and design.
Lead design discussions, drive architectural discussions, and influence technical decisions to simplify and streamline infrastructure.
Mentor engineers and help them grow technically.
Identify risks before they become incidents.