Multimodal ML Researcher — Physical-World Foundation Models

Archetype AI Inc.; San Mateo (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Archetype AI Inc. is building the world’s first physical AI platform to bring artificial intelligence into the real world. We seek an experienced, researcher-oriented ML candidate to help build systems end to end—from problem formulation and experimental design to model development, evaluation, and deployment.

This role is for someone highly self-directed, capable of independent scientific work, and excited to work on multimodal intelligence grounded in physical signals.

Qualifications

• Self-directed and comfortable operating in ambiguous problem spaces.
• Ability to independently perform strong scientific work from hypotheses to conclusions.
• Experience with end-to-end ML modeling including data, modeling, and evaluation.
• Experience with productionization or deployment of ML models.
• Multimodal experience preferred.
• Strong technical judgment and experimental rigor.

Responsibilities

• Build and improve multimodal foundation models that incorporate time-series / sensor data alongside language, vision, audio, and related modalities.
• Drive research and modeling efforts from problem definition through experimentation and evaluation.
• Own modeling work across data, modeling, and evaluation.
• Advance model architectures and training strategies for physical-world understanding and long-context reasoning.
• Drive and scale research experiments and modeling advances to production models that power diverse use cases in complex industrial scenarios.
• Contribute to research directions with potential for publication.

Skills

Self-directed

Experimental rigor

End-to-end modeling

Production deployment

Multimodal experience

Scientific problem solving

Ambiguity handling

Job description

Archetype AI Inc. is building the world’s first physical AI platform to bring artificial intelligence into the real world. We seek an experienced, researcher-oriented ML candidate to help build systems end to end—from problem formulation and experimental design to model development, evaluation, and deployment.

This role is for someone highly self-directed, capable of independent scientific work, and excited to work on multimodal intelligence grounded in physical signals.