Apple
Sr Software Architect Perception Systems
$185K–$325K
Cupertino, California · Senior · Full-time · Posted Oct 6, 2026
- Experience
- 10+ years building production systems software.
- Education
- BS or MS in Computer Science, Electrical Engineering, or equivalent experience.
About the job
Architect and lead the perception software stack that turns sensor inputs and perception and ML algorithms into fast, reliable, secure product experiences.
What you'll do
- Own the end-to-end architecture and technical direction of the perception stack, including a real-time graph-based dataflow runtime and system foundations.
- Bring perception algorithms and ML models from prototype to on-device production, with telemetry, profiling, privacy, and security built into the stack.
- Align hardware, software, and platform teams; guide milestones, engineering standards, and architectural decisions.
What they ask for
- 10+ years building production systems software in C/C++ or Swift, with deep knowledge of concurrency, scheduling, memory management, and IPC.
- Track record shipping latency-sensitive or real-time systems with measurable performance and reliability targets; experience designing dataflow or algorithm-graph runtimes across heterogeneous compute.
- Experience designing telemetry, tracing, and profiling; working knowledge of classical perception and robotics; experience deploying ML models into production runtimes.
- Demonstrated technical leadership and program-milestone partnership; clear communication. BS or MS in Computer Science, Electrical Engineering, or equivalent experience.
Nice to have
- Real-time Linux or RTOS experience; Darwin/XNU internals, secure execution environments, or microkernel-based isolation.
- Consumer hardware at scale; robotics middleware and logging; ML evaluation pipelines; familiarity with learned perception, multimodal models, or agentic AI workflows.
Pay
$184,700 and $324,800 (USD base salary)
Tools
C/C++, Swift, CPU, GPU, Neural Engine, PREEMPT_RT, ROS 2, DDS
AI focus
Architect perception and ML infrastructure; productionize on-device ML models and perception algorithms, balancing latency, accuracy, power, and memory. Preferred exposure to learned perception, multimodal models, or agentic AI workflows.
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