Staff Embedded ML Engineer, Edge AI

About SimpliSafe We’re a high-tech home security company that’s passionate about protecting the life you’ve built and our mission of keeping Every Home Secure. And we’ve created a culture here that cares just as deeply about the career you’re building. Ours is a no ego culture of collaboration and innovation where those seeking their next challenge can find big opportunities and make a huge impact on the lives of all those who we protect. We don’t just want you to work here. We want you to grow and thrive here. We’re embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done. Why are we hiring? Well, we’re growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure.  About the Role We are seeking a highly motivated and experienced Embedded Machine Learning Engineer to join our growing Edge AI team. As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV architectures and more about making models fast, power-efficient, stable, and shippable on real embedded hardware (outdoor cameras and doorbells). You will operate across the stack (from model runtime integration down to kernel/operator optimization, memory movement, scheduling, and accelerator utilization) to deliver reliable real-time behavior under tight compute, memory, bandwidth, and thermal constraints across device tiers. Responsibilities: Own the embedded deployment and performance of on-device ML inference for outdoor monitoring workloads (real-time video/event pipelines). Optimize end-to-end inference performance across CPU/DSP/NPU/GPU (as applicable): latency, throughput (FPS), memory footprint, power, thermals, startup time, and stability. Perform kernel/operator-level optimization: vectorization (e.g., SIMD/NEON), tiling, cache-friendly memory layouts reducing bandwidth and memory copies, optimizing post-processing fusing ops, minimizing synchronization/overhead, thread scheduling Integrate and maintain ML models within embedded pipelines: model import/export validation, operator compatibility, graph transforms runtime integration in C/C++ (including pre/post-processing) robust error handling, watchdogs, and safe fallback behavior Drive quantization and deployment readiness from an embedded perspective: validate INT8/FP16 paths, calibration flows, numerical accuracy checks debug quantization edge cases and operator mismatches on target runtimes Build tooling for profiling, benchmarking, and regression tracking on devices: per-layer timing, memory tracking, thermal/perf tests, CI gating automated performance regression gating across device tiers and firmware versions Partner closely with ML engineers to translate model changes into deployment impact; provide constraints and design guidance that improve deployability and performance. Provide Staff-level leadership: set performance standards, lead technical reviews, mentor engineers, and influence platform roadmap for on-device ML. Qualifications: 8+ years of experience in embedded systems and/or performance engineering, with experience shipping production software on constrained devices. Strong C/C++ expertise with deep knowledge of low-level performance topics: CPU architecture, memory hierarchy, concurrency, and real-time considerations. Demonstrated experience optimizing ML inference on embedded targets, including operator/kernel tuning and end-to-end pipeline optimization. Familiarity with modern vision model families (transformer-based detectors such as DEIM/DFINE/RT-DETR series and CNN-based detectors such as YOLO family or similar) sufficient to optimize their execution characteristics (tensor shapes, attention/conv patterns, post-processing). Experience with on-device inference runtimes and deployment workflows (e.g., TFLite, ONNX Runtime, TensorRT or vendor runtimes), including operator support constraints and graph-level transformations. Strong debugging and profiling skills (perf, flame graphs, hardware counters, tracing) and ability to drive performance investigations to closure. Ability to lead cross-functionally across ML, firmware, and hardware teams; comfortable defining benchmarks/KPIs and making tradeoffs. Bonus Points: Experience with embedded accelerators and vendor toolchains (DSP/NPU compilers, delegates, GPU compute, custom runtimes). SIMD expertise (ARM NEON/SVE), hand-tuned kernels, or experience with libraries like XNNPACK/QNNPACK/oneDNN/CMSIS-NN (or equivalents). Experience with quantized inference (INT8) at scale: calibration strategies, numerical debugging, overflow/underflow handling, and accuracy-performance tradeoffs. Experience with camera/doorbell pipelines: ISP/video decode/encode, DMA/zero-copy buffers, multi-threaded real-time streaming. Exposure to OS/firmware constraints (embedded Linux, RTOS), power management, thermal throttling behavior, and performance under sustained load. Security/privacy experience for edge devices (secure boot/TEE boundaries, model protection, safe telemetry). Experience building performance regression systems and device-lab automation for continuous benchmarking. What Values You’ll Share Customer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them. Aim High - Always challenging ourselv