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AlphaSense is seeking a Staff Product Security Engineer to lead the design and implementation of secure, scalable, and trustworthy products spanning AI, data, and cloud-native systems. You will work closely with engineering, data science, and infrastructure teams to embed security by design throughout the product lifecycle, sitting at the intersection of AI/ML security, secure product development, and container/cloud-native protection.
Key responsibilities include embedding robust security practices throughout the software and AI development lifecycle (SDLC); leading secure design reviews, threat modeling, and risk assessments for AI-driven products, APIs, and backend services; and partnering with engineering and product teams to ensure security, privacy, and compliance by design. You will build and maintain security automation and governance frameworks that integrate seamlessly into development workflows, and architect security controls for AI/ML systems, including model training, data pipelines, and inference environments.
You will identify and mitigate AI-specific attack vectors such as data poisoning, model inversion, prompt injection, and model theft. Collaborate with governance and compliance teams to align with ethical AI principles and frameworks like NIST AI RMF and the EU AI Act. Implement model provenance, integrity, and auditability controls to ensure responsible and secure AI operations. Partner with DevOps and SRE teams to secure service meshes, container networking, and secrets management.
Additional responsibilities include driving software supply chain security, including artifact integrity, dependency management, and vulnerability reduction; building internal frameworks for continuous assurance and real-time vulnerability management; defining and maintaining reference security architectures for microservices, APIs, and AI-powered systems deployed in the cloud; mentoring teams on secure coding, containerization best practices, and AI risk management; and promoting a security-first culture through advocacy, documentation, and training.
Required qualifications: 7+ years of experience in product or application security engineering; deep understanding of secure SDLC, threat modeling, and secure architecture design; proven expertise with AWS cloud security concepts and best practices; strong experience with container security, orchestration, and runtime protection; proficiency in Python, Java, and/or JavaScript for security automation, code review, and tooling; experience securing AI/ML pipelines, data workflows, or model-serving infrastructure; familiarity with DevSecOps and continuous integration/deployment environments; familiarity with encryption fundamentals including symmetric and asymmetric cryptography, TLS/mTLS, key management, and secrets handling; and demonstrated ability to drive cross-functional security initiatives.
