Job Description
Are you ready to define the standard for the next generation of technology? 2026 Industries is pioneering the future of autonomous systems and next-gen cloud infrastructure. We are looking for a visionary Principal Systems Architect to lead our engineering division into 2026 and beyond.
In this pivotal role, you will not just build software; you will architect the ecosystems that will power the world's most demanding digital transformations. You will bridge the gap between theoretical AI capabilities and practical, scalable engineering solutions.
Join us in Seattle and help us build the legacy of tomorrow.
Responsibilities
- Strategic Vision: Define the long-term technical roadmap for 2026 Industries, aligning engineering goals with business objectives and futuristic market trends.
- System Design: Lead the end-to-end design of complex, distributed systems, ensuring high availability, security, and performance at scale.
- Tech Stack Leadership: Evaluate and integrate emerging technologies (AI, Quantum Computing interfaces, Edge IoT) into our core architecture.
- Team Mentorship: Foster a culture of innovation and excellence by mentoring senior engineers and architects, conducting code reviews, and driving technical best practices.
- Cross-Functional Collaboration: Work closely with product managers, data scientists, and stakeholders to translate business requirements into robust technical blueprints.
- Operational Excellence: Establish and monitor key performance indicators (KPIs) for system health and guide the team in implementing DevOps and CI/CD pipelines.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field; PhD preferred.
- Experience: 10+ years of experience in software architecture, with at least 5 years in a Principal or Staff Engineer role.
- Technical Proficiency: Deep expertise in distributed systems, microservices architecture, and cloud platforms (AWS, Azure, or GCP).
- Programming: Proficiency in high-level languages such as Python, Go, or Rust.
- AI Integration: Strong understanding of machine learning operations (MLOps) and how to integrate AI models into production environments.
- Soft Skills: Exceptional communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.