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Principal Software Engineer - AI Control Tower; AICT

Job in Los Angeles, Los Angeles County, California, 90001, USA
Listing for: ServiceNow
Full Time position
Listed on 2026-10-18
Job specializations:
  • Software Development
    Software Architect, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 221000 - 387000 USD Yearly USD 221000.00 387000.00 YEAR
Job Description & How to Apply Below
Position: Principal Software Engineer - AI Control Tower (AICT)

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, Service Now is the AI control tower for business reinvention. Our Service Now AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better.

We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

The Role

The AI Control Tower is Service Now's central hub for governing every AI model, agent, and dataset across the enterprise. We're building the infrastructure that enterprises will depend on as agentic AI moves from experiment to production.

We're looking for a Principal Engineer who has built 1.0 products before: architecting from first principles, solving deep technical hurdles with early customers, and building technical foundations that compound adoption over time. You'll report to the Director of Engineering, AICT, and drive the technical architecture for critical product areas that will become standard infrastructure for how enterprises run AI at scale.

You are AI-native. You don't just understand AI conceptually — you use it daily, you build with it, and you bring that instinct into how your team designs and ships.

What you get to do in this role:

Technical Architecture & Leadership Lead the architectural design and implementation of core AICT platform components. You'll translate complex product requirements into scalable, observable technical plans and failure recovery patterns. You set the technical standard for how we build governance infrastructure.

1.0 to Adoption Drive the full arc from early release to meaningful customer adoption. You understand that shipping a 1.0 is only half the job — the other half is learning fast, iterating based on what real customers do (not what they say), and building the feedback loops that compound adoption over time.

AI-Native Engineering Set the bar for how we use AI in engineering itself. From AI-assisted code review and automated testing to designing platform features that treat AI agents as first-class users, you lead by example in leveraging AI to improve both delivery velocity and software quality.

Mentorship & Technical Excellence Act as the strong technical voice who raises the bar around you. You run deep-dive architecture reviews and provide clear technical guidance. You represent AICT in technical forums and contribute to long-term platform strategy.

Cross-Functional Execution Work closely with Product Management, Design, and partner engineering teams to align on dependencies and ship coherent experiences. Represent engineering in planning and review forums with senior leadership.

To be successful in this role you have:

10+ years in software engineering, with a proven track record of shipping complex enterprise-grade SaaS products and managing their evolution through scale.

  • Deep technical foundation in cloud-native SaaS architecture: multi-tenant design, large-scale API design (REST/gRPC), Kubernetes orchestration, observability patterns, and multi-cloud platforms (AWS, Azure, GCP).
  • Distributed systems & event-driven architecture expertise, with focus on scalability, reliability, fault tolerance, and operational observability.
  • Deep expertise in database schema design, data modeling, and modern data platforms, including relational, graph, vector, columnar, OLAP, and streaming data architectures, with the ability to design scalable data models for AI and enterprise workloads.
  • Startup mindset:
    Dire…
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