1X is a leader in humanoid robotics, creating humanoid robots for the home as a first step in developing general purpose robots
1X’s mission is to create an abundance of labor through safe,…
1X is a leader in humanoid robotics, creating humanoid robots for the home as a first step in developing general purpose robots
1X’s mission is to create an abundance of labor through safe,…
Robotics engineering focused on humanoid robots and AI systems for domestic and broader labor applications.
Founded
2015
Industry
Robotics Engineering
Funding Track Record
Series A2
Series A2 led by the OpenAI Startup Fund (reported $23.5M)
Participation reported from Tiger Global and Norway-based investors
Series B- 2024-01-11
$100M
Reported participation from EQT Ventures and other global investors; reported additional fundraising activity increased disclosed proceeds beyond $100M by 2025
Investor Signal
“Participation from prominent investors including the OpenAI Startup Fund, Tiger Global, and EQT Ventures”
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Who you are
M.S. or higher in Engineering, Computer Science, Robotics, or related field
10+ years of experience in AI/ML, robotics, or autonomous systems, with focus on safety-critical systems
Strong programming skills in Python and familiarity with ML frameworks
Experience working with real-world deployed learning-based systems
Deep understanding of:
ML robustness
Generalization failures
Edge cases and failure modes
Experience analyzing safety risks in human-interacting systems
Ability to operate at the intersection of AI, systems engineering, and safety
Experience with autonomous vehicles or robotics safety
Familiarity with safety frameworks (e.g., FMEA, FTA, SOTIF, UL 4600)
Experience with adversarial ML or AI security
Background in formal methods or verification for ML systems
Experience defining runtime safety systems or guardrails for AI
What the job involves
Benefits
Health, dental, and vision insurance
401(k) with company match
Paid time off and holidays
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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 gracefully under uncertainty, and remain robust in complex, human-facing environments
This is a high-impact, deeply technical role focused on advancing AI safety for embodied systems
You will work at the intersection of autonomy, safety engineering, and real-world deployment, partnering closely with AI, robotics, and security teams
You will report to the Director of Robot Safety
Identify and assess AI-specific hazards in end-to-end autonomy systems
Define and enforce safety constraints for AI-driven robot behavior involving humans, objects, and environments
Partner with Functional Safety to translate AI risks into system-level requirements and mitigations
Collaborate with AI teams to build runtime guardrails that validate and constrain AI-generated actions
Evaluate risks from:
Dataset bias
Distribution shift
Model drift
Rare and edge-case failure modes
Work with Cybersecurity to assess risks from:
Adversarial inputs
Prompt injection
Misuse scenarios
Provide input on residual risk, uncertainty, and confidence levels in AI behavior
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
What Success Looks Like:
AI systems operate within well-defined safety envelopes
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
