Principal Engineer, AI Native Quality & Safety,

Accountabilities •

• Partner with engineering, platform, Information Security, Site Reliability Engineering (SRE), and Compliance teams to design and implement layered safety controls that protect systems even when multiple safeguards fail.

• Build pre-production verification frameworks, including AI evaluations, invariant tests, and risk-based test selection, while maintaining a mandatory baseline of non-skippable invariants.

• Develop automated safety mechanisms throughout development and delivery so that system integrity does not depend on slow or inconsistent manual reviews.

• Lead the implementation of a “Ratify Continuously, Expose Deliberately” delivery model, incorporating continuous shadow soaking, ratified releases, and deliberate per-tenant exposure.

• Define critical system invariants and engineer controls that make prohibited or unsafe states unreachable.

• Build agentic systems that reduce continuous integration (CI) bottlenecks by quarantining flaky tests and moving full test suites to nightly execution.

• Identify ambiguous safety challenges, frame the underlying problems, and develop original technical approaches.

• Increase AI agent autonomy in proportion to improvements in verification capabilities.

• Implement risk-based evaluation and approval processes for pull requests (PRs).

• Design integrity-protected communication channels in which authorization comes only from signed sources and permission brokers, treating all agent inputs and tool outputs as data.

• Ensure AI agents have no authority over the safeguards that constrain them, including CI gates, branch protection, and audit loggers.

• Require privileged actuators to independently verify agent claims about environments, approvals, and test status before executing actions.

• Test, monitor, and observe AI models in production environments, with containment and rollback mechanisms for unsafe behavior.

• Establish engineering practices that make quality a systemic default rather than relying on individual heroics.

• Mentor and challenge Staff-level and more senior engineers, helping them expand their technical judgment, ownership, and impact.

• Work with engineering and product leadership to balance development speed with production risk.

• Create durable technical artifacts, including runbooks, decision logs, and tests, that other teams can maintain and evolve.

• Contribute across product domains, applying knowledge and lessons from one area to solve challenges in another.

Requirements

• At least 8 years of experience building and scaling complex software systems.

• Bachelor's degree in Computer Science, Information Security, or a related field, or one of the following equivalent education and experience pathways:

• Master's degree with at least 6 years of relevant experience.

• PhD with at least 4 years of relevant experience.

• Equivalent practical experience.

• Demonstrated experience using AI as a primary development collaborator.

• Strong technical judgment and the ability to define strategy, develop original solutions, and lead complex engineering initiatives.

Preferred Qualifications

• Experience designing safety-critical, high-assurance, or redundant systems.

• Experience building CI/CD pipelines and test infrastructure that improve engineering quality and development velocity at scale.

• Experience designing evaluations for large language models (LLMs) or AI agents.

• Experience with production-system observability, monitoring, and reliability practices.

• Knowledge of security controls such as least-privilege access, signed provenance, and policy-as-code.

• Experience with progressive delivery approaches, including canary releases, traffic mirroring, and automated rollback.

• Experience improving testing standards through practices such as Test-Driven Development (TDD), Behavior-Driven Development (BDD), load testing, stress testing, chaos testing, invariant testing, or mutation testing.

• Experience working in highly regulated industries, such as financial services.

• Demonstrated success leading large, cross-functional or cross-pillar technical initiatives.

• Strong written communication skills and a consistent practice of producing useful, durable technical documentation and decision artifacts.

• A leadership approach that empowers others, encourages independent judgment, and avoids unnecessary dependency.

• High standards for the quality, reliability, and safety of AI-generated output.

Additional Requirements

• Ability to travel up to 10% for team gatherings and internal developer conferences.

• Reliable internet access when working remotely outside an office location.

Benefits

• Annual U.S. salary range of $177,000–$290,000, with actual compensation depending on location, skills, qualifications, experience, and other relevant factors.

• Discretionary bonus, typically paid annually.

• Restricted Stock Units (RSUs) granted at the time of hire.

• 401(k) retirement plan with company match.

• Comprehensive employee benefits package.

• Flexibility to work remotely from any location within the United States, subject to country-of-employment requirements.

• Opportunity to shape AI-native engineering practices and build safety systems for complex enterprise software.

• Collaboration with experienced engineering, security, platform, SRE, and compliance professionals.

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