Archetype AI

Design, implement, and maintain high-performance distributed systems supporting AI inference at scale. | Requires 7+ years of experience in backend or distributed systems, with expertise in cloud environments, systems programming, and performance optimization. | 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 at the Series A stage and is 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 We’re looking for a highly motivated backend engineer with a passion for building performant, scalable, and resilient distributed systems. You’ll work closely with researchers, ML engineers, and product teams to bring cutting-edge AI capabilities into production—at scale, with reliability, and under real-world constraints. This is an opportunity to own critical services, optimize for latency and throughput, and contribute to some of the most advanced systems in production today. Core Responsibilities Architect, implement, and maintain distributed systems that support high-throughput, low-latency AI model inference and data services. Partner with ML researchers and product teams to turn experimental models into production-grade services. Continuously optimize performance across GPU clusters, cloud infrastructure, and backend systems. Build tooling and observability to monitor system health, identify bottlenecks, and proactively resolve instability. Introduce new techniques, architectures, and best practices to push the limits of scalability, efficiency, and reliability. Own problems end-to-end—from design to deployment—with a strong bias toward quality, automation, and continuous improvement. Balance rapid iteration on early-stage systems with long-term maintainability and architectural soundness. Contribute to a culture of engineering excellence, mentorship, and team-first collaboration. 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 Proven track record of scaling systems through rapid growth and rebuilding or refactoring for new demands. Proficiency in systems programming languages (e.g., Rust, C++) and scripting environments (e.g., Python). Experience designing internal tools or platforms to support developer productivity and experimentation. Strong product intuition, and ability to collaborate closely with cross-functional teams including research and design. Familiarity with modern ML stacks and hardware acceleration (e.g., PyTorch, CUDA). 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.

Lead the design and scaling of core backend systems for AI platform, collaborating with research and product teams, and mentoring engineers. | 7+ years of experience in backend or distributed systems, cloud experience, strong debugging and observability skills, ownership of complex technical problems. | 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.