Senior Software Engineer – Compute Engineering

ServiceNow•Santa Clara, CA

1d•$143,200 - $243,400

About The Position

ServiceNow is hiring across multiple engineering levels, with final level alignment based on experience, technical expertise, demonstrated impact, and interview performance. The role is part of Compute Engineering, which builds the scheduling, capacity, and performance systems that run the ServiceNow platform across private and public cloud. The engineer will develop software-defined, declarative infrastructure for compute at scale and contribute to compute scheduling and resource-management systems. The work includes building capacity data tools, testing performance and load, improving compute efficiency and reliability, and collaborating with networking, orchestration, and SRE teams. The position also involves design reviews, on-call rotations, post-incident improvements, and using AI tools in development and analysis, while exploring how machine learning could improve capacity or placement decisions. At the senior level, the engineer delivers well-defined features and components with guidance, independently writes, tests, and debugs code within a system, and takes work through to production. Staff-level expectations include owning a component or feature area end to end, leading design discussions, mentoring engineers, and identifying risks before they become incidents.

Requirements

Experience leveraging or critically considering how to integrate AI into work processes, decision-making, or problem-solving.
At least 5 years of related software engineering, cloud, or infrastructure automation experience with a bachelor's degree; or at least 3 years with a master's degree; or a PhD with 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.
Learning mindset and motivation to build new skills and make improvements for internal and external customers.
Excellent oral and written communication skills.
Integrity and honesty, and commitment to upholding ServiceNow's values.
For staff-level consideration: ability to drive architectural discussions and influence technical decisions to simplify and streamline infrastructure.
For staff-level consideration: experience leading the end-to-end design and delivery of a component or feature area.
For staff-level consideration: 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 machine-learning-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 machine learning to improve capacity or placement decisions.
At the staff level, own a component or feature area end to end, lead design discussions, mentor engineers, and identify risks before they become incidents.