Sr. Software Systems Safety Engineer

Apptronik•Sunnyvale, CA

6d•$200,000 - $240,000

About The Position

Apptronik is hiring a Sr. Software Systems Safety Engineer to own safety assurance for software and AI used in its humanoid robots, from architecture analysis through verification evidence. The role analyzes software and AI safety architectures, leads safety analyses, and evaluates whether available evidence supports safety claims. It applies IEC 61508-3 and ISO/IEC TS 22440 to safety-critical software and AI-enabled systems. The engineer collaborates on system-level risk assessments, allocates risk-reduction measures to software and AI elements, and owns software safety requirements breakdown, architecture, analyses, and evidence review. The role also defines the software safety lifecycle, evaluates software and AI/ML assurance, maintains requirements traceability and the software portion of the safety case, and supports product safety and compliance teams with certification and demonstrations of standards conformity.

Requirements

Demonstrated experience analyzing and assuring safety-critical software architectures, including software safety analyses and safety case development.
Practical application of the IEC 61508-3 or ISO 26262-6 software lifecycle, including software safety activities and qualification of existing software and tools.
Strong knowledge of embedded software architectures, including RTOS, mixed-criticality partitioning, watchdogs, communication buses, and diagnostics.
Hands-on experience with FMEA/FMEDA, FTA, and dependent failure analysis, and reviewing verification evidence such as fault injection, software-in-the-loop and hardware-in-the-loop testing, and test coverage.
Working knowledge of safety for AI/ML-enabled systems, such as ISO/IEC TS 22440.
Experience with requirements and traceability tools such as JAMA.
Ability to work cross-functionally and make sound, risk-based engineering decisions.
Experience with product development processes and product lifecycle management, including Agile, CMMI, and Waterfall, supporting teams in meeting organizational milestones.
Bachelor’s degree in Systems Engineering, Computer Engineering, Electrical Engineering, Computer Science, Robotics, or a related field, or equivalent experience.
At least 8 years of relevant engineering experience, including substantial responsibility for safety assurance or analysis of safety-critical software in complex electromechanical products.

Nice To Haves

Master’s degree preferred.
Experience with IEC 62061, ISO 13849, ISO 10218, ANSI/A3 R15.06, or ISO 25785-1, as relevant.
Familiarity with ISO/PAS 8800, UL 4600, or SOTIF.
Familiarity with Git, CI/CD, and static analysis.
Certification or third-party assessment experience preferred.

Responsibilities

Collaborate on system-level risk assessments and allocate risk-reduction measures to software and AI elements.
Break down software-domain safety requirements into subsystem- and element-level requirements.
Analyze and define software safety architecture, including partitioning, freedom from interference, watchdogs, diagnostics, plausibility and timing monitoring, communication integrity, startup and shutdown behavior, and fault handling.
Lead software safety analyses, including software FMEA, fault tree analysis, dependent and common-cause failure analysis, and timing and resource analysis.
Specify safety-related software behavior, such as monitors, diagnostics, and safe-state logic, and review software designs against IEC 61508-3 requirements for the target SIL/SC.
Define the IEC 61508-3 software safety plan and lifecycle, and assess team conformance through reviews and audits, including assessment of in-house, open-source, and third-party libraries, RTOS, and middleware.
Assess software systematic capability and review verification evidence, including coverage, static analysis, fault injection, and software-in-the-loop and hardware-in-the-loop test results, for adequacy against safety claims.
Apply the AI safety lifecycle (ISO/IEC TS 22440) to AI/ML-enabled functions, defining safety architecture, independence of safety functions from learned components, and monitoring safety mechanisms.
Assess AI/ML safety evidence, including data and model assurance, performance limitations, and operational design domain.
Own software requirements traceability and the software portion of the safety case, including standards gap assessments.
Support product safety and compliance teams in certification and demonstrations of standards conformity.