we are looking for a Senior Security Engineer.
As a Senior Product Security Engineer, you will help ensure that security is built into the products, platforms, AI systems, and clinical workflows that support healthcare providers and patients at scale. This is a hands-on technical role focused on securing real systems in production, influencing architecture, and partnering closely with engineering, AI, DevOps, product, quality and regulatory.
You will work across cloud-native services, medical imaging workflows, healthcare integrations, AI/ML pipelines, APIs, data flows, and regulated product development processes. Your work will directly support ability to deliver secure, reliable, and trusted clinical AI solutions to healthcare organizations worldwide.
Responsibilities:
Secure product and clinical workflows end-to-end - Work directly with engineering and product teams to identify, prioritize, and remediate security risks across services, APIs, medical imaging workflows, AI outputs, data flows, and customer-facing product features.
Drive secure-by-design architecture - Provide practical security guidance on authentication, authorization, tenant/customer isolation, encryption, secrets management, service-to-service communication, audit logging, and secure data handling.
Strengthen cloud-native security - Improve security across cloud environments, Kubernetes, containers, IAM, network segmentation, workload identity, infrastructure-as-code, logging, monitoring, and cloud security posture management.
Embed security into the development lifecycle - Integrate, tune, and operationalize security tooling across CI/CD pipelines, including SAST, SCA, IaC scanning, container scanning, secrets detection and build/release integrity controls.
Own vulnerability management as an engineering system - Prioritize vulnerabilities based on exploitability, product exposure, customer impact, clinical risk, and regulatory relevance. Drive remediation with engineering teams and reduce recurring issues through root-cause improvements.
Secure AI/ML-driven features and data pipelines - Partner with AI, data, and platform teams to identify risks related to training and inference data, model inputs and outputs, model misuse, data leakage, access to sensitive datasets, pipeline integrity, and production monitoring.
Improve software supply-chain security - Reduce risk across open-source dependencies, third-party components, third-party algorithms, container images, build systems, artifact repositories, release pipelines, and deployment processes.
Mentor and enable engineers - Help developers understand security risks, make secure design decisions, and take ownership of security in their code, services, and infrastructure.
Communicate risk clearly - Translate technical security findings into clear risk, impact, and trade-off language for engineering, product, leadership, quality, regulatory, and customer-facing stakeholders.