Principal Engineer, AI Native Quality & Safety
New
J
JobgetherAI software
Flexibility to work remotely from any location within the United States, subject to country-of-employment requirements.Full-TimePrincipal
SalaryAnnual U.S. salary range of $177,000–$290,000
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Job Details
- Experience
- At least 8 years of experience building and scaling complex software systems. Master's degree with at least 6 years of relevant experience; PhD with at least 4 years of relevant experience.
- Required Skills
- CI/CD
Requirements
- At least 8 years of experience building and scaling complex software systems.
- A bachelor's degree in Computer Science, Information Security, or a related field, or equivalent practical experience.
- Alternatively, a master's degree with at least 6 years of relevant experience or a PhD with at least 4 years of relevant experience.
- Demonstrated experience using AI as a primary development collaborator.
- Strong technical judgment and ability to define strategy, develop original solutions, and lead complex engineering initiatives.
- Experience designing safety-critical, high-assurance, or redundant systems is preferred.
- Experience building CI/CD pipelines and test infrastructure is preferred.
- Experience designing evaluations for large language models or AI agents is preferred.
- Knowledge of least-privilege access, signed provenance, and policy-as-code is preferred.
- Experience with progressive delivery, testing practices, production observability, monitoring, and reliability is preferred.
- Ability to travel up to 10% for team gatherings and internal developer conferences.
- Reliable internet access when working remotely outside an office location.
Responsibilities
- Partner with engineering, platform, Information Security, Site Reliability Engineering, and Compliance teams to design and implement layered safety controls.
- Build pre-production verification frameworks, including AI evaluations, invariant tests, and risk-based test selection.
- Develop automated safety mechanisms throughout development and delivery.
- Lead implementation of 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 CI bottlenecks by quarantining flaky tests and moving full test suites to nightly execution.
- Design integrity-protected communication channels and ensure agents cannot control the safeguards that constrain them.
- Test, monitor, and observe AI models in production, with containment and rollback mechanisms for unsafe behavior.
- Mentor Staff-level and more senior engineers and create maintainable runbooks, decision logs, and tests.
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