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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.
Job Responsibility:
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
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
Requirements:
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
Nice to have:
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
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 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
Participation in our annual bonus program, equity, and other forms of compensation, in addition to a full range of medical, retirement, and lifestyle benefits