Note: The job is a remote job and is open to candidates in USA. SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated. They are seeking a Senior AI Engineer who will design, build, and ship AI-powered product features while integrating advanced AI capabilities into their products.
Responsibilities
- Design, build, and ship customer-facing AI-powered product features using TypeScript, Go, and AWS, owning quality and reliability end-to-end
- Architect and implement agentic workflows, LLM pipelines, and intelligent automation that help customers surface faster, more actionable security insights
- Integrate foundation models into existing product surfaces with a practical eye toward latency, cost, output quality, and evaluation
- Use AI-native development tools (Cursor, Claude Code, and similar) as a core part of your workflow, setting the standard for how this team builds
- Lead architecture and code reviews with a focus on production-grade reliability, security, and maintainability
- Partner with product and design to translate user needs into compelling AI-driven experiences, contributing product judgment alongside technical execution
- Own projects end-to-end from concept through production, including monitoring, iteration, and improvement post-launch
- Stay current with the AI tooling and foundation model landscape and bring relevant advances into our roadmap and practices
Skills
- 5+ years of professional software engineering experience with a strong track record shipping customer-facing product features
- 2+ years of hands-on experience building AI-powered applications in production, including LLM integration, prompt design, and output evaluation
- Proficiency in TypeScript and strong backend engineering fundamentals
- Demonstrated ability to own complex projects end-to-end across the full software development lifecycle
- Experience designing and operating distributed systems in cloud environments (AWS preferred)
- Hands-on experience with CI/CD, containerization (Docker, Kubernetes), and production deployment practices
- Strong computer science fundamentals including systems design, data structures, and networking
- Fluency with AI-native development workflows including AI-assisted coding tools as part of day-to-day engineering
- Clear, direct communication with both technical and non-technical stakeholders
- Experience with agentic AI systems, multi-agent orchestration, or retrieval-augmented generation (RAG)
- Familiarity with LLM evaluation frameworks and production observability for AI systems
- Background in cybersecurity, threat intelligence, or compliance-focused products
- Experience with high-scale data systems and large structured datasets
- Full-stack delivery experience and comfort working across the product surface
Benefits
- Stock options
- Health benefits
- Unlimited PTO
- Parental leave
- Tuition reimbursements
- Annual performance-based incentive compensation awards
- Equity
Company Overview