Apptronik
Sr. Software Systems Safety Engineer
$200K–$240K
Sunnyvale, California +1 · Senior · Full-time · Posted Oct 30, 2026
- Location
- Sunnyvale, California
Sunnyvale, California, United States - Experience
- 8+ years of relevant engineering experience, including substantial responsibility for safety assurance or analysis of safety-critical software in complex electromechanical products.
- Education
- Bachelor’s Degree in Systems Engineering, Computer Engineering, Electrical Engineering, Computer Science, Robotics, or a related field (or equivalent experience); Master’s preferred.
About the job
Own safety assurance for software and AI in humanoid robots, from architecture and safety analyses through verification evidence and safety-case development.
What you'll do
- Define and analyze software safety architectures, requirements, and safety-related behaviors; lead software safety analyses.
- Apply the AI safety lifecycle to AI/ML-enabled functions and assess their safety evidence.
- Review verification evidence, maintain software requirements traceability and the software safety case, and support certification.
What they ask for
- Demonstrated experience assuring safety-critical software architectures, performing safety analyses, and developing safety cases.
- Practical application of IEC 61508-3 or ISO 26262-6 software lifecycles, including safety activities and qualification of existing software and tools.
- Strong knowledge of embedded software architectures and hands-on experience with FMEA/FMEDA, FTA, dependent failure analysis, and verification evidence.
- Bachelor’s degree in a related field or equivalent experience.
Nice to have
- Master’s degree.
- Familiarity with ISO/PAS 8800, UL 4600, or SOTIF; certification or third-party assessment experience preferred.
Pay
$200,000 - $240,000 USD base salary
Tools
IEC 61508-3, ISO/IEC TS 22440, ISO 26262-6, IEC 62061, ISO 13849, RTOS, JAMA, Git
AI focus
Apply the AI safety lifecycle to AI/ML-enabled functions, define safety architecture and monitoring mechanisms, and assess evidence on data and model assurance, performance limitations, and operational design domain.
Model-written summary · the original posting is authoritative