Lead Embedded ML Engineer, Edge AI and Real-Time Inference

SimpliSafe; Boston (MA); Hybrid; USD 185,500 - 244,600; Full time

Benefits offered by this job

Free home security system

Professional monitoring

ERGs

Job summary

SimpliSafe in Boston is seeking an experienced Embedded Machine Learning Engineer to join our Edge AI team. You will lead on-device inference and optimization for outdoor monitoring on real embedded hardware, delivering real-time performance under tight compute, memory, and thermal constraints.

This role requires 8+ years in embedded systems, strong C/C++, and hands-on experience with ML runtimes (TFLite, ONNX Runtime, TensorRT).

Qualifications

• 8+ years of experience in embedded systems or performance engineering.
• Strong C/C++ expertise with low-level performance knowledge.
• Experience optimizing ML inference on embedded targets and end-to-end pipelines.

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, memory footprint, power, thermals, startup time, and stability.
• Perform kernel/operator-level optimization: vectorization (ARM NEON/SVE), tiling, cache-friendly layouts, reduced bandwidth and memory copies, fused ops, minimized synchronization and overhead, thread scheduling.
• Integrate and maintain ML models within embedded pipelines: validation, operator compatibility, graph transforms, robust error handling, watchdogs, and safe fallback behavior.
• Drive quantization and deployment readiness: validate INT8/FP16 paths, calibration flows, numerical accuracy checks, and debug quantization edge cases.
• Build tooling for profiling, benchmarking, and regression tracking on devices: per-layer timing, memory tracking, thermal/perf tests, CI gating, device-tier regression.
• Partner with ML engineers to translate model changes into deployment impact; provide constraints and design guidance for deployability.
• Provide staff-level leadership: set performance standards, lead reviews, mentor engineers, influence platform roadmap.

Skills

Embedded systems

C/C++ expertise

Performance engineering

ML inference on embedded targets

On-device deployment runtimes

Profiling & debugging

Tools

TFLite

ONNX Runtime

TensorRT

oneDNN / XNNPACK

Embedded Linux

Job description

SimpliSafe in Boston is seeking an experienced Embedded Machine Learning Engineer to join our Edge AI team. You will lead on-device inference and optimization for outdoor monitoring on real embedded hardware, delivering real-time performance under tight compute, memory, and thermal constraints.

This role requires 8+ years in embedded systems, strong C/C++, and hands-on experience with ML runtimes (TFLite, ONNX Runtime, TensorRT).