Worldscape Technology
Engineering Development Lead - Full Stack
$160K–$200K
Redmond, Washington · On-site · Full-time · Posted Apr 15, 2026
- Experience
- 15+ years; 5+ years managing multiple projects or portfolio teams; 3+ years in agile, fast-paced environments.
- Education
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
About the job
Leads full-stack engineering delivery and architecture for Worldscape’s scalable distributed-cloud platform, while managing and mentoring engineers and remaining hands-on in development.
What you'll do
- Lead system design, solution architecture, sprint delivery, code reviews, and performance and reliability improvements.
- Manage, mentor, and grow engineering teams; coordinate with internal and external stakeholders and contribute to product roadmaps.
- Support customers through issue triage and on-call practices, and foster DevOps ownership of platform health and uptime.
- Spend at least 50% of work time actively coding and contributing to development.
What they ask for
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
- 15+ years of experience delivering technology solutions, practice development, architecture, consulting, and/or a technology domain.
- Experience shipping distributed cloud experiences across large-scale software solutions; 5+ years managing multiple projects or portfolio teams.
- 3+ years in agile, fast-paced environments and hands-on full-stack development experience with the listed languages and frameworks.
Nice to have
- Master’s degree in computer science or a related technical field; experience with nsjail, Firecracker, or MS Orleans.
- Experience with vector databases, semantic search, prompt engineering, reinforcement learning, planning algorithms, or multi-agent coordination.
Pay
USD 160,000–200,000 per-year-salary (base salary)
Tools
C#, Java, Typescript, React, Next.js, Node.js, nsjail, Firecracker
AI focus
Experience with vector databases, semantic search, prompt engineering, reinforcement learning, planning algorithms, and multi-agent coordination is preferred; the platform also incorporates reward-driven AI and intelligent agents.
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