
Archetype AI helps businesses perceive and reason about physical environments using real-time sensor data to detect issues and automate responses. The company develops Newton, a proprietary Largeβ¦

Archetype AI helps businesses perceive and reason about physical environments using real-time sensor data to detect issues and automate responses. The company develops Newton, a proprietary Largeβ¦
Focus: Physical AI: foundation models that interpret real-world sensor data
Product: Newton LBM and B2B SaaS platform (API, Agent Toolkit, edge deployment, private fine-tuning)
Headquarters: Palo Alto, California
Launch year: 2023
Known funding: Seed (~$13M) and later reported $35M round; total funding reported ~$48M
Real-world sensor interpretation, behavior understanding, and automation for physical environments.
2023
Technology, Information and Internet
13000000
Seed round reported with participation from Hitachi Ventures and the Amazon Industrial Innovation Fund
35000000
Reported later round with participation from strategic and institutional investors
βIncludes institutional VCs and strategic corporate investors (e.g., Venrock, IAG Capital Partners, Hitachi Ventures, Amazon Industrial Innovation Fund, Samsung Ventures, Bezos Expeditions)β
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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 Palo Alto, 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 donβt see a role that exactly fits you below you can contact us directly with your resume via jobsarchetypeaiio.
About The Role Archetype AI is seeking a hands-on Evaluation Lead to build and assess model performance for physical AI. You will design and implement advanced evaluation techniques for assessing the strengths and weaknesses of real-world AI models, and build and scale evaluation frameworks to rapidly test and generate reports on model performance. Responsibilities include partnering closely with research and engineering teams to develop evaluation methodologies, analytically assessing and improving test datasets, uncovering model weaknesses or risks, and tracking competitive industry benchmarks. This is a high-impact role for someone who thrives in a fast-paced AI environment and wants to directly influence our path as we scale our AI technologies and business.
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Minimum Qualifications
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Drive Benchmarking & Evaluation
Design and implement rigorous evaluation methodologies and benchmarks for measuring model effectiveness, reliability, alignment, and safety
Lead evaluation of model performance, ranging from offline experiments to full production model testing
Build & Scale Evaluation Frameworks
Design and oversee the pipelines, dashboards, and tools that automate model evaluation
Design and oversee tools for A/B model testing, regression testing, and production model performance
Lead Evaluation Strategy
Develop and implement strategies for evaluating physical AI models that can scale across a broad range of real-world use cases, sensor types, and edge cases
Plan, run, and oversee evaluations, across internal teams and external customers
Drive edge case discovery, red-teaming, safety, privacy, and risk evaluation - feeding back knowledge to key stakeholders in research and engineering teams