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Salary: USD 150,000 - 250,000 / annual
Cadence is a clinical AI company automating chronic disease treatment. The company partners with 20+ leading health systems, serves 100,000+ patients, and has been recognized by TIME as a Top 100 HealthTech Company and LinkedIn as a Top Startup (#4, 2025).
You will build and scale security infrastructure that embeds compliance and risk controls directly into the Cadence platform. Working embedded within Product and Platform Engineering teams, you'll design and implement critical security capabilities spanning cloud infrastructure, AI governance, GRC automation, and secure healthcare integrations.
Key responsibilities include:
- Design and implement security controls across cloud infrastructure, Kubernetes, CI/CD systems, endpoints, identity systems, secrets management, and platform security primitives
- Build and evolve Cadence's AI governance framework, including model development controls, safeguards, bias and reliability considerations, and post-deployment surveillance to ensure AI systems are safe, fair, and aligned with patient safety outcomes
- Translate complex security risks into clear business-level recommendations for technical and non-technical stakeholders, surfacing patterns and emerging threats
- Set standards for secure engineering practices across Cadence by designing scalable security primitives, golden paths, and reusable controls that enable teams to build securely by default
- Help set and defend prioritization across security infrastructure workstreams in partnership with engineering leadership
- Partner with CISO and senior leaders across Product, Engineering, Clinical, and Legal to set direction for responsible platform scaling
Required qualifications: 4+ years software engineering experience with 2+ years focused on security infrastructure, platform security, or security engineering in high-growth or regulated environments. Bachelor's or Master's in Computer Science, Engineering, or related field (or equivalent). Strong expertise in cloud security, Kubernetes, CI/CD pipeline security, identity and access management, secrets management, and security primitives at scale. Experience with AI governance, model risk, dataset controls, bias considerations, and responsible AI frameworks in regulated or patient-facing environments preferred. Ability to navigate the full stack, identify patterns, design scalable solutions, and set prioritization across workstreams.
