Principal Software Engineer – Compute Engineering

ServiceNow•Santa Clara, CA

About The Position

The Principal Software Engineer joins ServiceNow’s Compute Engineering organization, which builds scheduling, capacity, and performance systems for the company’s platform across private and public cloud. The role sets technical direction across multiple teams or a major compute area, shapes architecture, mentors senior engineers and architects, and influences decisions across geographies. The engineer designs and evolves compute schedulers and resource-management systems; develops capacity models and forecasts; establishes performance and scale testing; and applies AI and machine learning to forecasting, anomaly detection, and placement optimization when they provide an advantage over simpler approaches. The role also develops declarative, software-defined infrastructure and evaluates tools and methods to improve compute efficiency and reliability. This position partners with networking, orchestration, SRE, and FinOps teams; guides technical decisions and design reviews; and balances delivery speed with long-term durability. It owns production reliability and incident response for compute systems, leads post-incident improvements, and mentors architects and senior engineers.

Requirements

15+ years of related software engineering, cloud, or infrastructure automation 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.
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; use of 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, and ability to articulate a compelling technical vision.
A learning mindset and motivation to build new skills and make meaningful 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 schedulers 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 machine learning to forecasting, anomaly detection, and placement optimization where they outperform simpler approaches.
Write and maintain declarative, software-defined infrastructure at scale, using 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, surface architectural risks early, and balance shipping 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 build the next layer of technical leadership.
Set technical direction across multiple teams or a major compute area, shape architecture, and influence decisions across geographies.