Staff Embedded ML Engineer, Edge AI

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.

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 transformations, runtime integration in C/C++, 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 ourselves and others to raise the bar.
• No Ego - Maintaining a "no job too small" attitude, and an open, inclusive and humble style.
• One Team - Taking a highly collaborative approach to achieving success.
• Lift As We Climb - Investing in developing others and helping others around us succeed.
• Lean & Nimble - Working with agility and efficiency to experiment in an often ambiguous environment.

What we offer

• A mission- and values-driven culture and a safe, inclusive environment where you can build, grow and thrive.
• A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families.
• Free SimpliSafe system and professional monitoring for your home.
• Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change.