Staff Backend Engineer - Distributed Systems at Archetype AI

San Mateo, California, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Apr, 26

Salary

0.0

Posted On

23 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Backend Engineering, Distributed Systems, Cloud Environments, Performance Optimization, Reliability, Operational Excellence, Debugging, Observability, Technical Strategy, Mentoring, Collaboration, Architecture Design, Data Pipelines, GPU Clusters, Automation, Concurrency

Industry

technology;Information and Internet

Description

About Archetype AI Archetype AI is developing the world's first AI platform to bring AI into the real world. Formed by an exceptionally high-caliber team from Google, Archetype AI is building a foundation model for the physical world, a real-time multimodal LLM for real life, transforming real-world data into valuable insights and knowledge that people will be able to interact with naturally. It will help people in their real lives, not just online, because it understands the real-time physical environment and everything that happens in it. Supported by deep tech venture funds in Silicon Valley, Archetype AI is currently pre-Series A, progressing rapidly to develop technology for their next stage. This presents a unique and once-in-a-lifetime opportunity to be part of an exciting AI team at the beginning of their journey, located in the heart of Silicon Valley. Our team is headquartered in San Mateo, California, with team members throughout the US and Europe. We are actively growing, so if you are an exceptional candidate excited to work on the cutting edge of physical AI and don’t see a role that exactly fits you below you can contact us directly with your resume via jobsarchetypeaiio. About the Role As a Staff Backend Engineer, you will lead the design and scaling of the core backend systems that power our AI platform. You will collaborate closely with ML researchers, product teams, and other engineers to bring cutting-edge AI models into production at scale, ensuring performance, reliability, and operational excellence. This role goes beyond coding: you will own complex systems end-to-end, influence architectural decisions, drive technical strategy, mentor other engineers, and elevate the overall engineering culture. Core Responsibilities Lead the architecture, design, and implementation of distributed systems supporting high-throughput, low-latency AI model inference and data services. Collaborate with ML researchers and product teams to transition experimental models into production-grade systems. Define technical strategy and best practices for backend systems, including GPU clusters, cloud infrastructure, and distributed data pipelines. Drive performance optimization, reliability, and operational excellence across large-scale systems. Build internal tools, monitoring, and observability frameworks to proactively detect and resolve issues. Introduce innovative architectures, techniques, and automation to maximize scalability, efficiency, and reliability. Mentor engineers, lead by example, and foster a culture of engineering excellence, knowledge sharing, and collaboration. Balance rapid iteration on early-stage systems with long-term architectural soundness and maintainability. Take ownership of end-to-end problem solving—from design through deployment—ensuring high quality and robust delivery.. Minimum Qualifications 7+ years of professional software engineering experience, with a focus on backend or distributed systems. Deep understanding of distributed systems fundamentals—concurrency, consistency, replication, fault tolerance, networking. Experience building and operating production-grade systems at scale in cloud environments (e.g., Azure, AWS, GCP). Strong debugging, instrumentation, and observability skills across distributed systems. Demonstrated ownership of complex technical problems and ability to learn and adapt quickly. Preferred Qualifications 7+ years of professional software engineering experience, with deep expertise in backend or distributed systems. Strong understanding of distributed systems fundamentals: concurrency, consistency, replication, fault tolerance, and networking. Experience building and operating production-grade systems at scale in cloud environments (AWS, GCP, Azure). Advanced debugging, instrumentation, and observability skills across complex distributed systems. Proven ownership of complex technical problems and ability to drive them to completion. Experience mentoring engineers and influencing architectural decisions across teams. What We Value Ownership – You take initiative, follow through, and care deeply about quality and outcomes. Motivation – You’re driven to solve complex problems and continuously raise the bar for yourself and your team. Excellence – You bring discipline, clarity, and rigor to your craft—and help others do the same. Collaboration – You work well with others, mentor generously, and contribute to a high-trust, high-performance culture.

Responsibilities

As a Staff Backend Engineer, you will lead the design and scaling of core backend systems for the AI platform. You will collaborate with ML researchers and product teams to ensure performance and reliability in production environments.