AI Systems Researcher (Embedded Intelligence)

1X Technologies AS•Palo Alto, CA

About The Position

Since its founding in 2015, 1X has been at the forefront of developing advanced humanoid robots designed for household use. Our mission is to create an abundant supply of labor through safe, intelligent humanoids. We strive for excellence in all we do, solving some of the hardest problems in robotics with the world’s most talented individuals. Every part of our robots is designed and produced in house, from motor coils to AI, reflecting our vertically integrated approach. At 1X, you will own real projects, be recognized for your achievements, and be rewarded based on merit. As an AI Systems Researcher (Embodied Intelligence) on the 1X Labs team, your role is: Your role is to push embodied intelligence toward human-level dexterity by working at the level where sensing, actuation, learning, and physical structure form a single closed loop. This role exists because dexterity is not a policy problem. It is a system problem. Intelligence in a humanoid does not live in a network alone—it emerges from how perception is structured, how actions are generated and constrained, and how the body itself participates in learning. Your work targets that loop directly. You work at the frontier of foundation models and multimodal learning, but you are not bound to existing architectures. You are expected to break with them when performance gaps demand it. You let failures in real systems—latency, instability, brittleness, lack of contact understanding—guide what models, representations, and interfaces need to exist next. Neuroscience and biological motor control are reference points, not inspiration slides. You explicitly embrace the difference between models built for language and models that must operate in tight sensor–actuator loops. You understand that embodiment imposes constraints—bandwidth, delays, noise, compliance—that fundamentally shape how intelligence must be structured. This role sits inside an interdisciplinary lab, embedded with hardware, sensing, biomechanics, and prototyping teams, while having direct access to 1X’s world-class AI organization. The loop between hypothesis, hardware change, experiment, and learning is intentionally short. You are expected to use that loop to unlock capabilities that cannot be reached by model-centric work alone. You are exceptional in AI—but you are here because that excellence makes you dissatisfied. You can see the matrix: how intelligence is orchestrated across sensing, processing, and actuation, and how changing the system reshapes what learning can achieve. You want to build machines where intelligence is inseparable from the body it lives in.

Requirements

PhD or equivalent depth of contribution in machine learning, robotics, control, or a closely related field.
Clear record of excellence in AI research (e.g. influential publications, widely adopted methods, or deployed systems).
Demonstrated, hands-on contributions to sensing and/or actuation systems, not just downstream learning.
Substantial experience working with real robotic hardware in closed-loop settings.
Proven ability to reason across abstraction layers—from learning objectives and representations down to physical interaction and dynamics.
Evidence of work that advanced system capability, not just algorithmic benchmarks.

Nice To Haves

Prior work on dexterous manipulation, tactile sensing, or whole-body control.
Experience combining learning with custom hardware or novel sensing modalities.
Familiarity with biological motor control or neuroscience as engineering reference systems.

Responsibilities

Develop learning systems for embodied intelligence that operate in tight sensor–actuator loops.
Drive progress toward human-level dexterity by addressing system-level limitations, not just model performance.
Co-design sensing, actuation interfaces, and learning architectures with hardware and robotics teams.
Use real-world experiments to expose performance gaps and guide architectural decisions.
Break with existing model or control paradigms when they block progress toward physical capability.
Translate insights from experiments into changes across models, representations, sensors, and actuation.

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