Staff Embedded ML Engineer, Edge AI

SimpliSafe; Boston (MA); Sur place; USD 185 500 - 244 600; Plein temps

Avantages offerts par ce poste

Free home security system

Professional monitoring

ERGs

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Embedded systems

C/C++ expertise

Performance engineering

ML inference on embedded targets

On-device deployment runtimes

Profiling & debugging

Outils

TFLite

ONNX Runtime

TensorRT

oneDNN / XNNPACK

Embedded Linux

Description du poste

Overview

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. We foster a collaborative, growth‑oriented culture with opportunities to make a meaningful impact. We embrace a hybrid work model with core in‑office days and flexible remote work for the remainder of the week.

About the Role

We are seeking a highly motivated and experienced Embedded Machine Learning Engineer to join our Edge AI team. As a key contributor, you will lead on‑device inference and performance optimization of ML models powering outdoor monitoring in the home security space. The role focuses on 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, and thread scheduling.
• Integrate and maintain ML models within embedded pipelines: model import/export validation, operator compatibility, graph transforms, 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, and 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, and 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 and CNN‑based detectors) and their execution characteristics.
• 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‑functional efforts 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 familiarity with libraries like XNNPACK/QNNPACK/oneDNN/CMSIS‑NN.
• Experience with quantized inference (INT8) at scale: calibration strategies, numerical debugging, and accuracy‑performance tradeoffs.
• Experience with camera/doorbell pipelines: ISP/video decode/encode, DMA/zero‑copy buffers, multi‑threaded real‑time streaming.
• Understanding of OS/firmware constraints (embedded Linux, RTOS), power management, thermal throttling, and sustained performance.
• 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 employees and their families
• Free SimpliSafe system and professional monitoring for your home
• Employee Resource Groups (ERGs) that foster networking, mentorship, and development

The target annual base pay range for this role is $185,500 to $244,600. This range reflects our good‑faith estimate of pay for this role and may be adjusted based on experience, location, and other factors. Beyond base pay, we offer a Total Rewards package that may include bonuses, equity, and other benefits. Details can be found in our compensation information.

We’re committed to fair and equitable pay practices and pay transparency. We regularly review programs to ensure competitiveness and alignment with our values.

Equal employment opportunity statement: We welcome applications from all qualified individuals. We do not discriminate on any protected basis. If you need a reasonable accommodation to participate in the application or interview process, please contact [e-mail].