Staff Embedded ML Engineer, Edge AI

SimpliSafe seeks a Staff Embedded ML Engineer to lead on-device inference optimization for outdoor security cameras. You will deploy and optimize ML models on embedded hardware, ensuring real-time performance under tight constraints. This role involves kernel/operator tuning, quantization, and building benchmarking tooling, collaborating with ML and firmware teams.

• Own the embedded deployment and performance of on-device ML inference for outdoor monitoring workloads.
• Optimize end-to-end inference performance across CPU/DSP/NPU/GPU: latency, throughput, memory footprint, power, thermals, and stability.
• Perform kernel/operator-level optimization: vectorization, tiling, cache-friendly memory layouts, and post-processing fusion.
• Integrate and maintain ML models within embedded pipelines: model import/export, runtime integration in C/C++, error handling.
• Drive quantization and deployment readiness: validate INT8/FP16 paths, calibration, numerical accuracy.
• Build tooling for profiling, benchmarking, and regression tracking on devices.
• Provide Staff-level leadership: set performance standards, lead technical reviews, mentor engineers.

• 8+ years of experience in embedded systems and/or performance engineering.
• Strong C/C++ expertise with deep knowledge of CPU architecture, memory hierarchy, concurrency, and real-time considerations.