Product Security Engineer
- Role
- Security
- Experience
- Senior
- Employment
- Full-time
- Company size
- Startup
Open to US only. Set where you work from to check your eligibility.
No BS summary
StackAI is hiring a hands-on (Senior) Product Security Engineer to design, build, and harden the secure architecture at the core of the product, working as a technical partner to our Core engineering lead. This role involves writing production code and owning security-critical systems like encryption, key management, and customer data protection.
Core skills
Optional skills
ABOUT THE COMPANY
StackAI (by Asana) is a no-code AI workflow platform that empowers companies to build, deploy, and scale AI-powered workflows easily. With thousands of users and rapidly growing enterprise adoption, our mission is to democratize access to LLMs and bring AI into the hands of every business operator, not just developers. An Asana Company, we're building the foundational platform for the AI-driven future of work
ABOUT THE ROLE
At StackAI, security is how we earn the trust of the enterprises building AI assistants on our platform. We're hiring a hands-on (Senior) Product Security Engineer to design, build, and harden the secure architecture at the core of the product, working as a technical partner to our Core engineering lead.
This is a hands-on engineering role. You'll write production code and own the security-critical systems the whole platform depends on: encryption and key management, customer data protection, and how security is built into the way every team ships.
If you want deep ownership of these systems and the chance to harden and scale them as the platform grows, we'd love to meet you.
WHAT YOU'LL DO
- Own encryption and signing. Take ownership of our KMS, key management, BYOK, envelope encryption, and signing pipeline across both cloud and on-prem deployments—operating, hardening, and evolving them as the platform scales.
- Protect the most sensitive customer data. Extend our PHI/PII scrubbing and strengthen the data-protection foundations that regulated enterprises already rely on.
- Secure the storage layer. Own encryption at rest and tenant isolation.
- Keep security the default in how we ship. Maintain and expand the secure-by-default templates and reference implementations embedded in our SDLC—the ones engineers actually want to adopt.
- Threat-model the platform. Lead threat modeling on the seams between systems (the execution engine, connector trust boundaries, and multi-tenant isolation), using modern, AI-assisted threat-modeling tooling.
- Raise the bar on tooling. Push our scanning further on coverage, signal, and CI enforcement, so critical findings never reach production.
- Be the technical point of contact for security standards. Translate audit, compliance, and incident-response requirements into real implementation in our codebase.
WHAT WE'RE LOOKING FOR
- 4+ years building security-critical systems in production, with significant time spent implementing, not only reviewing or assessing.
- Practical depth in cryptography and key management: encryption, KMS, secrets handling, and signing in real systems.
- Secure architecture judgment: you can design and reason about secure systems and hold your own as a technical peer with senior engineers.
- Multi-tenant SaaS isolation experience, including the data-isolation guarantees regulated customers require.
- Strong secure-coding skills in our stack: Python on the backend, TypeScript/Node.js on the product surfaces.
- Comfortable wiring security checks and gates into CI/CD so security is enforced automatically in the pipeline.
Security engineering is broad. If you're strong on most of this and excited to grow into the rest, we'd like to hear from you, even if you don't check every box. Bonus points
- Cloud and API security fundamentals on GCP, Azure, or AWS.
- Securing on-prem, self-hosted, or air-gapped deployments.
- Experience in regulated domains (healthcare/PHI, finance, etc.).
- Familiarity with AI/LLM platform security: agent execution, connector trust boundaries, prompt and tool-call risk.
- Startup or growth-stage experience.
What you'll do
- Own encryption and signing. Take ownership of our KMS, key management, BYOK, envelope encryption, and signing pipeline across both cloud and on-prem deployments—operating, hardening, and evolving them as the platform scales.
- Protect the most sensitive customer data. Extend our PHI/PII scrubbing and strengthen the data-protection foundations that regulated enterprises already rely on.
- Secure the storage layer. Own encryption at rest and tenant isolation.
- Keep security the default in how we ship. Maintain and expand the secure-by-default templates and reference implementations embedded in our SDLC—the ones engineers actually want to adopt.
- Threat-model the platform. Lead threat modeling on the seams between systems (the execution engine, connector trust boundaries, and multi-tenant isolation), using modern, AI-assisted threat-modeling tooling.
What they require
- 4+ years building security-critical systems in production, with significant time spent implementing, not only reviewing or assessing.
- Practical depth in cryptography and key management: encryption, KMS, secrets handling, and signing in real systems.
- Secure architecture judgment: you can design and reason about secure systems and hold your own as a technical peer with senior engineers.
- Multi-tenant SaaS isolation experience, including the data-isolation guarantees regulated customers require.
- Strong secure-coding skills in our stack: Python on the backend, TypeScript/Node.js on the product surfaces.
Stack AI is a no-code drag-and-drop tool to quickly design, test, and deploy AI workflows that leverage Large Language Models (LLMs), such as ChatGPT, to automate any business process. Our core value is to make it extremely easy to build arbitrarily complex AI pipelines using a visual interface that allows you to connect different data sources with different AI models. Our customers use Stack AI to build applications such as: - Chatbots and Assistants: AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs. - Document Processing: apps to answer questions, summarize, and extract insights from any document, no matter how long. - Answer Questions on Databases: connect GPT-like models to databases (such as Notion, Airtable, or Postgres) and ask questions about them. - Content Creation: generate tags, summaries, and transfer styles or formats between documents and data sources.