Senior Software Engineer, MeridianLink

• 5+ years of professional software engineering experience, delivering features and infrastructure independently in production
• Hands-on experience building and maintaining CI/CD systems at org scale, preferably GitLab CI and/or Jenkins
• Experience building developer-facing tooling or platform services other engineers depend on
• Hands-on experience with LLM developer tooling: MCP, LLM APIs, agent orchestration, or AI harnesses (Claude Code, Cursor, Copilot Workspace, or equivalent)
• Deep proficiency in Python or TypeScript, with production experience sufficient to own and deliver real features
• Proficiency with Kubernetes and Helm at production scale on AWS or Azure
• Experience designing shared pipeline abstractions and CI/CD infrastructure used by multiple teams
• Familiarity with infrastructure-as-code tools (Terraform, Pulumi, or equivalent)
• Proficiency with standard development tooling: Git, Docker, automated testing, and modern scripting languages
• Active daily use of AI-assisted development tools
• Bachelor's degree in Computer Science, Software Engineering, or equivalent experience

Desirable

• This is a senior-level individual contributor on the Engineering Enablement team
• The team builds the shared CI/CD infrastructure, AI development tooling, and sandbox environments that hundreds of R&D engineers depend on
• A core part of that mission is advancing MeridianLink's AI-native development program — building the harnesses, agent infrastructure, and shared tooling that move engineering teams from ad-hoc AI usage toward autonomous, repeatable development pipelines
• This role owns a significant chunk of that platform and drives adoption across engineering teams
• This is a hands-on role: real code, real infrastructure, direct engagement with engineering teams. The measure of success is how much faster you make everyone else
• Senior individual contributors own their work end-to-end, identify problems before they're surfaced, and make the engineers around them better. Senior engineers at MeridianLink are active, daily users of AI-assisted development tools
• Owns features and infrastructure end-to-end: design through production release, limited guidance required
• Identifies edge cases and failure modes independently within assigned scope
• Participates actively in code review with constructive, specific feedback
• Surfaces blockers early rather than waiting for check-ins
• Writes tests that catch regressions without over-engineering the suite
• Monitors shipped work, responds to issues, and follows incidents to resolution
• Puts institutional knowledge into shared systems rather than individual heads
• Designs pipeline abstractions (templates, shared jobs, reusable configs) that work across multiple teams and tech stacks
• Reasons clearly about the tradeoffs between standardization and flexibility at org scale
• Keeps pipelines healthy, observable, and continuously improving
• Builds and maintains shared MCP servers, agent orchestration harnesses, and reusable skills and plugins
• Understands LLM developer tooling in practice: tool definitions, agent loops, prompt management
• Designs shared tooling with product thinking: requirements gathering, feedback triage, prioritized backlog
• Owns the shared infrastructure layer for autonomous AI agent environments: orchestration, provisioning, observability, cost controls, and security guardrails
• Partners with product teams on their individual sandbox configs while maintaining the platform underneath
• Treats engineers as customers: office hours, documentation, feedback loops
• Measures platform impact with DORA metrics, adoption rates, and time-to-productivity data
• Closes the gap between shipping tooling and driving adoption
• Own and evolve shared infrastructure: templates, shared jobs, abstractions, and standards across R&D
• Resolve systemic reliability issues: flaky tests, slow builds, caching inefficiencies
• Partner with teams during migrations and help them adopt shared abstractions without disrupting delivery
• Build and maintain shared MCP server infrastructure connecting AI harnesses to internal systems (Jira, Confluence, GitLab, internal APIs)
• Develop agent orchestration infrastructure: scheduling, observability, cost controls, security boundaries
• Build reusable harness skills, slash commands, and workflow scripts that ship as internal plugins
• Own the shared infrastructure for AI agent sandbox environments: container orchestration, environment templates, networking, resource management
• Build and maintain orchestration and admin tooling: provisioning, lifecycle management, health monitoring, cost tracking
• Implement security guardrails for data isolation between sandbox environments
• Drive AI tooling adoption through documentation, onboarding programs, office hours, and direct team engagement
• Maintain the internal best practices hub and AI development playbook
• Instrument platform usage and productivity metrics to measure whether investments are moving the needle
• Participate in design discussions and code reviews; give and receive feedback constructively
• Mentor other engineers on the team
• Contribute to documentation and onboarding materials that reduce tribal knowledge
• What Success Looks Like:
• Within the first few months, a successful hire is shipping CI/CD improvements teams are actively using and contributing meaningfully to the AI tooling platform. Over time, success is adoption: more teams on shared infrastructure, faster delivery, less one-off tooling being built in isolation
• Engineers who thrive here care about making other people more productive and find genuine satisfaction in watching adoption metrics climb