At NerdWallet, we’re building tools and experiences that help people make smarter financial decisions. As a Software Engineer focused on AI & Automation within our Security organization, you’ll build production software and AI-enabled systems that make security work more scalable, effective, and developer-friendly across NerdWallet.

You’ll apply strong software engineering fundamentals and hands-on AI experience to automate complex security workflows, build internal platforms and developer tooling, and help us rethink how security engineering operates in an AI-native environment. You do not need to come from a traditional security engineering background. We’re looking for an engineer who has built and operated production software and AI-enabled systems, is curious about security, and is excited to develop deeper security expertise while solving meaningful problems at scale.

You’ll have room to bring new ideas, influence how AI and automation practices evolve at NerdWallet, and shape a growing area within Security. This role reports to the Security Automation Manager.

Projects you may be working on in this position include:

• Designing and building AI-enabled workflows and multi-agent systems that automate security design review, threat modeling, policy Q&A, vulnerability analysis, and other high-value security processes.
• Building retrieval-augmented generation (RAG) and semantic search experiences across large code and documentation repositories.
• Creating automated code-review capabilities that help identify insecure patterns and improve software quality earlier in the development lifecycle.
• Building production Python services and APIs that power AI-enabled security workflows, with thoughtful architecture, authentication, authorization, testing, observability, and reliability.
• Designing and operating scalable pipelines that ingest, process, index, and retrieve large volumes of code, documents, and events.
• Improving AI system quality through evaluation frameworks, experiment management, prompt and context management, observability, and evidence-based iteration.
• Managing practical production tradeoffs such as latency, reliability, model/provider selection, token usage, and cost.
• Designing integrations with GitHub, Slack, Jira, Confluence, LLM providers, and internal services so security guidance fits naturally into engineering workflows.
• Applying security controls to AI applications, including prompt injection defenses, sensitive-data handling, access control, supply-chain considerations, and safe model inputs and outputs.
• Identifying new opportunities for software, automation, and AI augmentation across the Security team and independently moving high-value ideas from problem definition through production.

Where you can make an impact:

• Own technically meaningful software and AI initiatives from problem definition through production and ongoing operation.
• Help shape how AI and automation are used across NerdWallet Security, including when AI is the right solution and when it is not.
• Build tools and platforms that make security easier for engineering teams to adopt and use.
• Improve the speed, consistency, and quality of security workflows through thoughtful software design and automation.
• Grow into deeper application and AI security expertise while bringing strong software engineering judgment to the team.
• Communicate technical tradeoffs clearly and collaborate with engineers, security partners, and non-technical stakeholders.

Your experience:

• 3+ years of professional software engineering experience building and operating production software.
• Strong Python proficiency, including hands-on experience building production backend services and APIs.
• Experience designing maintainable backend architectures, including separation of concerns, request/response validation, authentication/authorization, error handling, testing, and observability.
• Experience developing distributed or cloud-based systems and making practical decisions about scalability, reliability, and performance.
• Hands-on experience building production AI-enabled applications or automations using LLM APIs, agents/tool use, RAG/retrieval, or comparable architectures.
• Experience defining, evaluating, and improving the quality of AI-enabled systems in production.
• Ability to work through ambiguity, identify opportunities independently, and move technical work from idea to implementation.
• Strong …