SecurityScorecard - Staff AI Engineer
Requirements
• 7+ years of software engineering experience with a track record of leading complex, high-impact technical projects end-to-end • Demonstrated ability to operate independently across the full product development lifecycle, from problem definition through production operations • 3+ years building and shipping AI-powered product features in production, including LLM integration, agentic systems, or intelligent automation • Strong product instincts: you think about user problems and business outcomes, not just technical implementation • Proficiency in TypeScript and strong backend engineering fundamentals with experience in distributed systems at scale • Fluency with AI-native development workflows (Cursor, Claude Code, or equivalent) as a primary engineering practice, not an experiment • Cloud infrastructure experience (AWS preferred) and comfort with the full production stack • Exceptional communication: able to drive alignment across product, engineering, and executive stakeholders • TypeScript / JavaScript, Go, AWS, Docker, Kubernetes, Terraform, ClickHouse, PostgreSQL • Prior experience in a Staff or Principal engineering role with cross-team technical influence • Background in cybersecurity, threat intelligence, or enterprise risk products • Experience building and operating agentic AI systems, multi-agent orchestration, or LLM evaluation infrastructure • Track record of defining engineering standards or leading technical strategy at the organizational level • Experience in high-growth, Series C/D+ companies where engineering velocity and quality both matter • Benefits:Specific to each country, we offer a competitive salary, stock options, Health benefits, and unlimited PTO, parental leave, tuition reimbursements, and much more!
Responsibilities
• Lead the design and delivery of major AI-powered product initiatives from concept through production, owning the full software development lifecycle • Define the technical architecture for AI systems at SecurityScorecard: LLM pipelines, agentic workflows, retrieval infrastructure, and evaluation frameworks • Set the standard for how the engineering organization builds with AI, including development practices, tooling choices, and quality bars • Use Cursor, Claude Code, and AI-native development tools as core instruments, and model what high-velocity, high-quality AI-augmented engineering looks like for the team • Collaborate directly with product leadership and the CTO organization to shape the AI product roadmap • Drive architecture reviews, establish cross-team engineering standards, and elevate the technical level of the teams you work with • Identify and eliminate bottlenecks in how AI features get built, tested, and shipped across the organization • Operate with Staff-level ownership: you see the whole problem, not just your part of it
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