Principal Engineer, AI Native Quality & Safety,

What You'll Do

Deliver outcomes

• Partner with engineering, platform, InfoSec, SRE, and Compliance teams to build layered safety controls, so systems stay safe even when multiple safeguards fail.
• Build pre-production verification, including evals, invariant tests, and risk-based test selection under a non-skippable invariant floor.
• Build automated safety valves into development and delivery, so safety does not depend on slow human review.

Define and drive technical strategy

• Lead the "Ratify Continuously, Expose Deliberately" model: continuous shadow soak, ratified releases, and deliberate per-tenant exposure.
• Define the invariants our systems must never violate, and design controls that make those states unreachable.
• Build Agentic Systems to remove CI bottlenecks by quarantining flaky tests and moving full suites to nightly runs.
• Frame ambiguous safety problems and propose original approaches to solve them.
• Increase agent autonomy only as verification improves.
• Risk based evaluation approval of PRs

Design AI-driven systems

• Enforce integrity-protected channels, where authorization comes only from signed sources and permission brokers, and all agent inputs and tool outputs are treated as data.
• Ensure agents have zero authority over the guardrails that constrain them, such as CI gates, branch protection, and audit loggers.
• Require privileged actuators to independently verify an agent's claims about environment, approvals, and test status before acting.
• Test, monitor, and observe AI models in real environments, with containment and rollback when behavior is unsafe.

Influence and lead

• Raise the technical bar by making quality the default rather than the result of heroics.
• Mentor and challenge Staff+ engineers to expand their scope, judgment, and impact.
• Partner with engineering and product leadership to balance velocity with production risk.
• Create durable artifacts such as runbooks, decision logs, and tests that can be handed off and evolved by others.
• Contribute across product domains, bringing context from one area to solve problems in another.

Minimum Qualifications

• 8+ years of experience building and scaling complex software systems.
• Bachelor's degree in Computer Science, Information Security, or a related field; or 6+ years and a Master's degree; or a PhD with 4+ years of experience; or equivalent practical experience
• Demonstrated experience using AI as a primary development collaborator.

Preferred Qualifications

• Experience designing safety-critical, high-assurance, or redundant systems.
• Experience building CI/CD and test infrastructure that improves engineering velocity and quality at scale.
• Experience designing evals for LLMs or AI agents.
• Experience with observability and monitoring for production systems.
• Experience with security controls such as least-privilege access, signed provenance, and policy-as-code.
• Experience with progressive delivery, including canary releases, traffic mirroring, and automated rollback.
• Experience improving testing standards through approaches such as TDD, BDD, load, stress, chaos, invariant, or mutation testing.
• Experience working in a highly regulated industry, such as financial services.
• Proven success leading large, cross-pillar technical initiatives.
• Strong written communication skills and a habit of creating durable, useful artifacts.
• A leadership style that empowers others rather than creating dependency.
• A strong quality bar for AI-generated output.

Travel Requirements & Working Conditions

• Up to 10% travel for team jams and internal developer conference
• Reliable internet access for any period of time working remotely, not in a Workiva office

How You'll Be Rewarded

Salary range in the US

• $177,000.00 - $290,000.00
• A discretionary bonus typically paid annually
• Restricted Stock Units granted at time of hire

401(k) match and comprehensive employee benefits package

• The salary range represents the low and high end of the salary range for this job in the US.
• Minimums and maximums may vary based on location.
• The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.