Staff Autonomy Safety Engineer - Robot Safety

United States·Haywardlead

OtherEngineer

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Quick Summary

Overview

Staff Autonomy Safety Engineer, Robot Safety Location: San Carlos, CA (On-site)Type: Full-time About 1X At 1X, we are building humanoid robots that work alongside humans to solve labor shortages and create abundance.

Key Responsibilities

We are hiring a Staff Autonomy Safety Engineer to lead safety assurance for machine learning–driven autonomy systems. You will ensure that perception, prediction, and decision-making systems operate safely under real-world conditions, degrade…

Technical Tools

cybersecuritymachine-learning

At 1X, we are building humanoid robots that work alongside humans to solve labor shortages and create abundance. Our robots operate in real-world, human environments—bringing AI out of simulation and into everyday life.

Responsibilities

~1 min read

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Collaborate with AI teams to build runtime guardrails that validate and constrain AI-generated actions

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Evaluate risks from:

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dataset bias
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distribution shift
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model drift
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rare and edge-case failure modes

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Work with Cybersecurity to assess risks from:

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adversarial inputs
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prompt injection
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misuse scenarios

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Provide input on residual risk, uncertainty, and confidence levels in AI behavior

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Help define safety strategies for real-world deployment of autonomous systems

This is a staff-level, hands-on technical role. We are looking for someone who can deeply analyze AI system behavior, define safety frameworks, and work directly with engineering teams to implement safeguards in production systems. You are expected to operate with high autonomy, influence cross-functional teams, and contribute directly to the safety of deployed robots.

Safety constraints are enforced in real-time decision-making systems

Robust mitigations exist for edge cases and failure modes

AI behavior is measurable, explainable, and auditable from a safety perspective

Strong collaboration between AI, Safety, and Security teams

Reduced risk from distribution shifts, adversarial inputs, and unexpected environments