Senior Software Engineer, AI Governance
Natera · US Remote
About this role
**Senior Software Engineer, AI Governance** **About the role** Natera is deploying AI into clinical, diagnostic, and patient-facing workflows at scale. As the portfolio grows, AI governance needs to be real—embedded controls in the platform, automated risk intake, runtime policy enforcement, and production monitoring to catch drift before it becomes an incident. This role builds that infrastructure. This is a hands-on software engineering role. You’ll design, build, and operate technical systems that make AI governance work at Natera—guardrails in the LLM Gateway, PHI/PII detection in the RAG infrastructure, automated risk scoring in the intake pipeline, observability dashboards, and incident response tooling. Roughly **75%** of your time is engineering; **25%** is process and cross-functional work. You report to the **Head of AI & Data Governance** and work closely with platform engineers, Legal, Privacy, RAQA, and business teams. --- **What you’ll do** **Build governance controls into the AI platform** - Design, build, and own the guardrail layer inside Natera’s **LLM Gateway** (content filtering, output validation, PHI/PII detection, prompt injection defenses, session retention, and audit logging). - Engineer the governance layer for **agentic runtime** and **RAG** infrastructure (policy enforcement hooks, output routing by risk tier, retrieval filtering, citation integrity checks, and PHI exposure prevention). - Instrument every control with **observability from day one** (what’s filtering, what’s flagging, what’s escalating, and what’s drifting from approved risk profiles). - Ensure controls meet **HIPAA, RAQA, and Natera data classification requirements** at design time. - Build governance as a **runtime capability**—the platform enforces policy; you define, implement, and verify it under real conditions. **Build the automated risk intake and monitoring systems** - Design and implement the automated **AI risk intake pipeline** (structured intake, automated risk scoring, tiering: Low / Medium / High / Critical, and routing to the right review path—aligned to **NIST AI RMF, CHAI, and the EU AI Act**). - Build and maintain the **AI Risk Register** as an engineering artifact (risk scores, treatment decisions, mitigation steps, and review history; queryable and auditable). - Define and build monitoring for every production AI use case (accuracy tracking, drift detection, misuse alerting, escalation thresholds) and ensure it’s instrumented and verified before go-live. - Build and maintain **AI incident response tooling** (automated detection, alert routing, decision logging, remediation tracking). **Run the risk assessment and governance review process** - Run the AI use-case intake process end to end: evaluate new use cases against the risk tiering model before build/deploy. - Facilitate high-risk use-case reviews with the **AI Governance Board** (Legal, Privacy, RAQA) and produce clear, time-bound recommendations and controls. - Keep the risk questionnaire, scoring rubric, and tiering criteria current as the portfolio and applicable frameworks/regulations/internal policies evolve. **Gate what gets bought or integrated** - Own technical due diligence for AI vendors and foundation model providers (data residency, model transparency, API security, contractual controls, regulatory alignment) before external systems connect to Natera data/workflows. - Maintain the vendor AI risk assessment framework and ensure contracts include AI
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