Backend Engineer - Distributed Systems

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, we are building a foundation model for the physical world, a real‑time multimodal LLM that transforms real‑world data into valuable insights and knowledge for natural interaction. It will help people in their real lives, not just online, by understanding the real‑time physical environment and everything that happens in it.

Supported by deep tech venture funds in Silicon Valley, we are at Series A, rapidly progressing to the next stage. Located in the heart of Silicon Valley, our headquarters are in San Mateo, California, with team members throughout the US and Europe. We are actively growing; 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.

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.

• 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.

• 5+ 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.

• 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).

• 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.