Staff CV Applied Research Engineer, Edge AI Job at SimpliSafe (Boston)

Job Description

We are seeking a highly motivated and experienced Computer Vision Applied Research Engineer to join our growing Edge AI team. As a key contributor, you will lead development of on-device machine learning for outdoor monitoring in the home security space. You will build and optimize computer vision models that run in real time on resource-constrained embedded devices like outdoor cameras and doorbell cameras, balancing accuracy with latency, memory, power, and reliability in challenging conditions (night, weather, motion blur, occlusions).

Job Responsibility

Lead end-to-end development of edge ML models for outdoor monitoring (e.g., person/vehicle/package detection, classification, tracking, segmentation, event understanding)
Architect, train, and deploy transformer-based vision models (e.g., compact ViTs, hierarchical transformers, DETR-style detectors) and hybrid CNN-transformer backbones optimized for embedded inference
Drive model efficiency through resource-aware design and training, including: Architecture: Token/patch reduction, efficient attention variants, early-exit / conditional compute
Training: distillation from large transformer teachers to edge students
Compression: Quantization (PTQ/QAT), pruning, mixed precision, and operator-aware optimization
Translate product requirements into model targets (accuracy, FPS, memory footprint, power/thermal) and ensure models meet budgets on doorbell/outdoor camera hardware
Partner with embedded/firmware and platform teams to integrate models into production pipelines
profile bottlenecks and improve end-to-end runtime performance
Define evaluation strategies tailored to outdoor edge deployments
perform failure analysis and improve long-tail robustness (nighttime, rain/snow, backlight, fast motion)
Set technical direction and raise engineering standards: best practices for experimentation, reproducibility, model/version management, and deployment readiness
mentor other ML engineers

Requirements

8+ years in applied ML/ML engineering, including shipping production CV models
Strong computer vision background with deep learning expertise across detection/classification/segmentation/tracking
Hands-on experience with vision transformers and/or DETR-style architectures, including practical knowledge of efficiency trade-offs for edge deployment
Demonstrated success deploying models in resource-constrained, real-time environments (embedded/mobile/IoT/edge)
Deep experience in model optimization: QAT/PTQ, distillation, pruning, compression, mixed precision, and hardware/runtime-aware training
Proficiency in Python and PyTorch and/or TensorFlow
ability to productionize models and collaborate with systems engineers (C++ experience strongly preferred)
Staff-level leadership: ability to drive ambiguous initiatives, align stakeholders, and mentor engineers

Nice to have

Expertise in efficient transformer techniques (e.g., attention approximations, windowed/local attention, KV caching where applicable, token merging, sparsity) and their deployment implications
Experience building model “ladders” across multiple chipsets/device tiers with consistent KPIs and automated regression testing
Experience with embedded inference tooling and runtimes (e.g., TFLite, ONNX Runtime, TensorRT) and model export/compatibility constraints
Familiarity with embedded accelerators and profiling (ARM NEON, DSP/NPU toolchains), kernel/operator tuning, and real-time video pipelines
Experience with long-tail data strategies (active learning, hard-negative mining) and edge reliability/telemetry feedback loops

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

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Staff CV Applied Research Engineer, Edge AI

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