Staff Embedded ML Engineer, Edge AI
SimpliSafe · Boston, MA
$185,500–$244,600from the description
Jul 15, 2026
Boston, MA
Jul 21, 2026
What this job asks for AI summary
A staff-level embedded ML engineering role focused on deploying and optimizing machine learning models on resource-constrained hardware — specifically outdoor cameras and doorbells. The work centers on inference performance across CPU, DSP, NPU, and GPU targets, covering kernel-level tuning, quantization, memory optimization, and building profiling and regression tooling. Suits engineers with deep C/C++ and embedded systems backgrounds who have shipped production ML on real devices.
Staff level · 8+ years · Boston-Cambridge-Newton, MA-NH · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 240 people in the Boston-Cambridge-Newton, MA-NH area plausibly meet what this posting asks for (software developers). range 50–360
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
What the occupation pays Median $169,742 (middle half $134,780–$187,781). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (9)
This role sits at the intersection of embedded systems engineering and ML deployment — it is primarily about shipping optimized inference software on constrained hardware (cameras/doorbells), which maps most naturally to Software Developers (15-1252). 15-1299 (Computer Occupations, All Other) is a plausible runner-up given the specialized embedded ML focus.
The posting explicitly frames this as a 'Staff-level' role with responsibilities for setting performance standards, leading technical reviews, mentoring engineers, and influencing platform roadmap — genuine Staff-level scope, not merely a Senior with a long experience requirement.
SimpliSafe is headquartered in Boston, MA; the role is hybrid (two core in-office days per week), so it is not fully remote.
C and C++ are listed together as a single requirement ('C/C++') and are both used in the stack; they are emitted as separate skills per extraction rules.
TFLite, ONNX Runtime, and TensorRT are listed as interchangeable runtime examples ('e.g., TFLite, ONNX Runtime, TensorRT or vendor runtimes') under the required qualifications — emitted as one skill with alternatives.
SIMD/NEON is listed under required qualifications as an example of vectorization expertise; ARM NEON/SVE and XNNPACK/QNNPACK/oneDNN/CMSIS-NN appear separately under Bonus Points.
Vision model families (YOLO, RT-DETR, DEIM, DFINE) are listed under required qualifications but framed as 'familiarity sufficient to optimize execution characteristics' — a softer gate; however, because they appear in the required qualifications section, they are treated as required. Given the 'familiarity' framing, this is a judgement call.
All Bonus Points skills (ARM NEON/SVE, XNNPACK family, INT8 quantization at scale, ISP/video pipelines, embedded Linux/RTOS, DSP/NPU toolchains, DMA/zero-copy, security/TEE, regression systems) are explicitly labeled as optional extras.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): ML inference optimization, CPU architecture.
Read the full posting
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