Distinguished Software Engineer, AI

SecurityScorecard

7h•$350,000 - $400,000•Remote

About The Position

SecurityScorecard is forming a new engineering team to rethink how the company builds and ships software in an environment where AI agents can write much of the code. This is a greenfield role for an engineer who will experiment with agentic coding workflows on production code, identify their failure points, build tooling and guardrails to address them, and help turn effective practices into standard workflows across engineering. As the team develops, it will take on product ownership and integrate with the wider engineering organization. The role reports to the CTO or an engineer leading the team; reporting lines may change as the team grows. The engineer will also design, build, and ship customer-facing AI-powered features using TypeScript, Go, and AWS. The work includes architecting agentic workflows, LLM pipelines, and intelligent automation; integrating foundation models into existing product experiences; and balancing latency, cost, output quality, and evaluation. The engineer will own projects from concept through production and post-launch monitoring and improvement, with a focus on quality, reliability, security, and maintainability. The role is remote and open to candidates in the United States, Canada, and Latin America.

Requirements

At least 10 years of experience building and shipping production software.
A track record at strong engineering companies, such as big tech companies or high-growth startups.
Evidence of driving team direction and taking initiative.
Deep computer science and system design fundamentals, including the ability to design distributed systems and explain their failure modes.
Extensive hands-on use of AI coding tools on real codebases, with informed views on their strengths and limitations.
Experience building with LLMs, including agents, tool use, evaluations, prompt design, and context design.
Proficiency in TypeScript and strong backend engineering fundamentals.
Comfort with ambiguity and a team whose structure and responsibilities may change.
Experience designing and operating distributed systems in cloud environments; AWS is preferred.
Hands-on experience with CI/CD, containerization using Docker and Kubernetes, and production deployment practices.
Clear, direct communication with technical and non-technical stakeholders.

Nice To Haves

Experience at both big tech companies and high-growth startups.
Experience with AWS.
Familiarity with Go, JavaScript, Docker, Kubernetes, Terraform, ClickHouse, and PostgreSQL.

Responsibilities

Run agentic coding workflows on production code and determine what works at SecurityScorecard's scale.
Build tooling, guardrails, evaluations, and review practices that enable trusted agent-written code.
Design systems for software development in which much of the code is written by agents, and determine where human review is needed.
Ship changes to SecurityScorecard products and use product areas as test cases for new engineering practices.
Pair with engineers across the organization, review their work, and help them adopt lessons and practices from the team.
Interview candidates and help determine who joins the team.
Document findings and practices so other teams can apply them.
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 gain faster, more actionable security insights.
Integrate foundation models into existing product surfaces while considering latency, cost, output quality, and evaluation.
Use AI-native development tools as a core part of the workflow and set the standard for how the team builds.
Lead architecture and code reviews focused on production-grade reliability, security, and maintainability.
Own projects from concept through production, including monitoring, iteration, and post-launch improvement.
Track advances in AI tooling and foundation models and bring relevant developments into the roadmap and practices.