Staff CV Applied Research Engineer, Edge AI

About the role

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).

Responsibilities

• 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.

Qualifications

• 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.

Bonus points

• 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.

Values you’ll share

- Customer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them.
- Aim High - Always challenging ourselves and others to raise the bar.
- No Ego - Maintaining a “no job too small” attitude, and an open, inclusive and humble style.
- One Team - Taking a highly collaborative approach to achieving success.
- Lift As We Climb - Investing in developing others and helping others around us succeed.

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 (For more information on our total rewards please click here)
- 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.

Pay

The target annual base pay range for this role is $183,300 to $268,800.

Benefits

We’re committed to fair and equitable pay practices, as well as pay transparency. We regularly review our programs to ensure they remain competitive and aligned with our values.