Senior AI Research Scientist — Sensor Data / Robotics

STRATOS Search; Palo Alto (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Archetype AI, based in Silicon Valley, is seeking an AI Researcher to design, train, and interpret large-scale models that learn from raw sensor streams. You will work on representation learning, multimodal learning, and foundation models across diverse sensing modalities.

You will collaborate with engineers and product teams to translate research advances into production systems, with a focus on scalable, real-world applications in a fast-paced, multidisciplinary environment.

Qualifications

• 8+ years of experience building ML/AI systems with real-world sensor data.
• Strong expertise in modern deep learning architectures, especially transformers and representation learning.
• Excellent research and experimentation skills with new approaches.

Responsibilities

• Design and train foundation models for sensor data and multimodal inputs.
• Develop representation learning and self-supervised methods for raw sensor streams.
• Adapt pretrained models to new datasets, sensing modalities and environments.
• Identify limitations and design experiments to drive improvements.
• Collaborate with engineers and product teams to deploy research.

Skills

Transformer architectures

Representation learning

Deep learning

Python

PyTorch

Research & experimentation

Communication skills

Tools

PyTorch

Job description

About Us

At Archetype AI, we’re building the world’s first physical AI platform to bring artificial intelligence into the real world. Our foundation model, Newton, understands the physical world through objective sensor data and generates real-time insights into complex physical behaviors, from industrial machinery and systems to wearable devices and smart environments. Formed by a high-caliber team from Google and backed by one of Silicon Valley’s most renowned venture funds, Archetype AI is in a Series A phase and rapidly advancing its technology for the next big leap. This is a unique opportunity to join an exciting, fast-growing AI team based in the heart of Silicon Valley.

Role Overview

We are building the next generation of foundation models for real-world sensor data. Our goal is to develop AI systems that can learn rich representations of complex environments from diverse physical measurements, including vibration, temperature, electrical signals, gases, video, and other sensor modalities, and use those representations to understand and reason about the physical world. We are looking for an AI Researcher to design, train, and interpret large-scale models that learn directly from raw sensor streams. This role combines deep learning research, large-scale experimentation, and hands-on system building, with the opportunity to shape core technology used across multiple real-world applications. You will work on problems at the intersection of representation learning, multimodal learning, foundation models, and physical-world sensing.

Key Responsibilities

• Design and train foundation models for sensor data, including multimodal architectures combining non-visual sensing modalities.
• Develop new approaches for representation learning, multimodal alignment, and self-supervised learning from raw sensor streams.
• Develop methods for adapting pretrained models to new datasets, sensing modalities, environments, and customer use cases, understanding and navigating trade-offs between data availability, model capacity, and performance.
• Identify model limitations, diagnose failure modes, and design experiments that drive measurable improvements.
• Collaborate closely with engineers and product teams to translate research advances into production systems.

Qualifications

• 8+ years of experience developing advanced ML/AI systems, with a focus on real-world sensor data.
• Strong expertise in modern deep learning architectures, especially transformers, representation learning, and large-scale model training.
• Strong research and experimentation skills, including designing and evaluating new approaches.
• Excellent programming skills in Python, with deep learning frameworks such as PyTorch.
• Ability to move quickly from idea → experiment → working prototype.
• Comfortable working in a fast-paced, multidisciplinary environment with a distributed team.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Familiarity with LLMs.
• Experience with self-supervised or contrastive learning for large unlabeled datasets.
• Experience with large-scale training infrastructure or distributed training frameworks.