simplisafe

simplisafe

Staff Embedded ML Engineer, Edge AI

Boston, MA · Staff+

Sponsorship not specified$186k-$245kDetected 1 day ago
C++LinuxMachine LearningNLPComputer VisionEmbedded SystemsSignal ProcessingLeadershipCollaboration

About the role

  • We are seeking a highly motivated and experienced Embedded Machine Learning Engineer to join our growing Edge AI team.
  • 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).

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:
  • Integrate and maintain ML models within embedded pipelines:
  • Drive quantization and deployment readiness from an embedded perspective:
  • validate INT8/FP16 paths, calibration flows, numerical accuracy checks
  • Build tooling for profiling, benchmarking, and regression tracking on devices:
  • 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.
  • A mission- and values-driven culture and a safe, inclusive environment where you can build, grow and thrive

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.
  • 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.
  • 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.
  • 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).
  • 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:
  • Exposure to OS/firmware constraints (embedded Linux, RTOS), power management, thermal throttling behavior, and performance under sustained load.

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).

Compensation

  • The target annual base pay range for this role is $185,500 to $244,600
  • This target annual base pay range represents our good-faith estimate of what we expect to pay for this role.
  • We use a market-based compensation approach to set our target annual base pay ranges and make adjustments annually.

Benefits

  • A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families (For more information on our total rewards please click here )

Company info

  • About SimpliSafe
  • 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.
  • And we've created a culture here that cares just as deeply about the career you're building.
  • Ours is a no ego culture of collaboration and innovation where those seeking their next challenge can find big opportunities and make a huge impact on the lives of all those who we protect.
  • We don't just want you to work here.
  • We want you to grow and thrive here.
  • We're embracing a hybrid work model that enables our teams to split their time between office and home.
  • Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday - working together in person and choosing where they work for the remainder of the week.
  • We all benefit from flexibility and get to use the best of both worlds to get our work done.
  • Why are we hiring?
  • Well, we're growing and thriving.
  • So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure.
  • We regularly review our programs to ensure they remain competitive and aligned with our values.
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This listing is sourced directly from simplisafe's careers page and normalized into a canonical job model.