Principal Software Engineer - Platform Engineering Workflow Automation

ServiceNow•Santa Clara, CA

5d•$221,200 - $387,100•Hybrid

About The Position

We build the workflow and automation engines that power how work gets done across the ServiceNow Platform. Our engines process billions of workflow transactions every month across enterprise customers, and we're now extending that foundation to support AI-native, agentic workflows. This is a team that cares about getting the architecture right, not just shipping features. We spend real time on performance, reliability, and making sure the platform holds up as customers push more volume and more complexity through it.

Requirements

15+ years of engineering experience, or equivalent.
Real experience building and running large-scale distributed systems: workflow engines, integration platforms, data-heavy products, or cloud infrastructure.
Solid grasp of system design for scale, reliability, observability, and production readiness, including database architecture at high transaction volume.
Strong depth in Java, Python, or a comparable language.
A track record of setting technical direction and influencing architecture across teams, even without a formal reporting line.
Comfortable leading through credibility and collaboration rather than title.
Good judgment on when to invest in long-term architecture versus when to just ship.
Experience mentoring senior engineers and building patterns that other teams actually reuse.
Experience with AI-powered products, agentic systems, or workflow automation platforms is a strong plus.
Comfortable reasoning about AI-assisted workflows: data quality, correctness, evaluation, safety, and where a human needs to stay in the loop.
15+ years of related engineering experience, or equivalent practical experience.
Strong experience building and operating large-scale distributed systems, workflow engines, integration frameworks, or cloud-scale infrastructure handling high transaction volume.
Deep understanding of system design for scale, reliability, observability, fault tolerance, data quality, security, and production readiness.
Strong technical depth in Java, Python, or similar programming languages.
Proven ability to define technical direction, influence architecture across teams, and translate ambiguous product strategy into executable engineering plans.
Ability to operate as a Principal IC by leading through technical credibility, collaboration, and influence rather than formal authority.
Strong judgment in balancing long-term architecture with pragmatic execution, customer impact, engineering velocity, and business priorities.
Experience mentoring senior engineers, raising engineering standards, and building reusable patterns that create leverage across teams.
Experience with AI-powered products, automation platforms, AI-assisted workflows, or agentic systems is strongly preferred.
Ability to reason about AI-assisted enterprise workflows, including data quality, workflow correctness, evaluation, observability, safety controls, and human-in-the-loop review.

Nice To Haves

Bonus if you've worked on large-scale enterprise customers or led complex platform migrations.
Experience with workflow automation, business process management, low-code/no-code platforms, or process/task orchestration at scale is a strong plus.
Experience working with large-scale enterprise customers, production systems, and complex migration or modernization efforts is a plus.

Responsibilities

Lead architecture and technical direction for problem areas across the portfolio, working with product and engineering leads to figure out what's blocking progress and where the real opportunities are.
Get hands-on. Build the prototypes and reference implementations that prove out an architectural idea before a whole team commits to it.
Test ideas with real experiments and real data, not just design docs. Use what you learn to shape the direction.
Help move existing workflow capabilities toward AI-native patterns: agentic execution, better use of context and data, more autonomous flows, and connect that to concrete engineering work.
Dig into cases where an agentic feature exists but isn't delivering the value it should. Figure out if it's the architecture, the data, the UX, or the cost model, and drive the fix.
Write down the patterns and tradeoffs that work so other teams don't have to rediscover them.
Sit in on design reviews and tough calls across teams. Push back with data when something looks risky, and help teams weigh shipping speed against long-term durability.
Work across engineering teams to share what's working and help good patterns spread faster.