Staff Software Engineer - Developer Experience
New
J
JobgetherDeveloper tools
Based in the United StatesFull-TimeStaff
SalaryBase salary: $158,000–$248,000 USD, depending on role-related skills, experience, qualifications, work location, and market conditions. Additional compensation includes a bonus and equity.
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Job Details
- Required Skills
- DockerPythonJavaJavascriptJenkinsKubernetesCI/CDTerraformGitHub Actions
Requirements
- Have deep conceptual and hands-on expertise integrating AI into the software development lifecycle, from planning through production maintenance.
- Have strong programming skills in Python, Java, JavaScript, or similar languages, with a focus on clean, maintainable, production-grade tools.
- Have extensive practical experience with CI/CD orchestration platforms such as GitHub Actions or Jenkins.
- Have strong knowledge of infrastructure-as-code tools such as Terraform.
- Have experience with containerization technologies including Docker and Kubernetes.
- Demonstrate the ability to use data and engineering metrics, particularly DORA metrics, to improve delivery performance and framework reliability.
- Bring autonomy and initiative, and be comfortable navigating ambiguity and developing practical solutions.
- Have technical leadership, communication, collaboration, and mentoring skills across multidisciplinary engineering teams.
- Preferred: experience managing complex software states and isolating or reproducing environments at enterprise scale.
- Preferred: experience building internal applications, wrappers, or developer plugins around large language models (LLMs).
- Preferred: technical leadership or close collaboration across distributed engineering teams in North America and India.
- Preferred: familiarity with service integration and contract testing tools, and developer catalog platforms such as Backstage.
Responsibilities
- Lead adoption of AI-native development workflows and coach engineers on orchestrating parallel AI agent sessions and subagents.
- Collaborate with platform engineering, R&D, and security teams to streamline handoffs and replace manual approvals with automated, governed workflows.
- Design and operationalize Markdown-driven artifact pipelines that provide transparent, auditable software lifecycle records.
- Architect deterministic build-time and deployment hooks for safety boundaries in AI-assisted development.
- Integrate AI-powered code review into pull request workflows using structured policies and CI runners.
- Build verification frameworks and visual feedback loops for AI agents to test, identify defects, iterate, and validate results.
- Develop CI evaluation suites to assess AI configurations against engineering tasks and detect prompt or model regressions.
- Implement production monitoring and automated triage workflows for reliability, performance, and control issues.
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