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.