Staff Edge AI Computer Vision Scientist

Simplisafe; Boston (MA); Hybrid; USD 183,300 - 268,800; Full time

Benefits offered by this job

Free SimpliSafe system & monitoring

ERGs for networking & mentorship

Total rewards package

Job summary

SimpliSafe is seeking a Staff Computer Vision Scientist, Edge AI to lead on-device machine learning for outdoor monitoring in real‑time, resource‑constrained environments. You will build and optimize computer vision models for outdoor cameras and doorbells, balancing accuracy with latency and power use in challenging conditions.

You will architect and deploy transformer-based vision models, drive model efficiency, and collaborate with embedded teams to integrate models into production pipelines.

Qualifications

• 8+ years in applied ML/ML engineering with CV models.
• Deep learning expertise across detection/classification/segmentation/tracking.
• Proficiency with vision transformers and edge deployment trade-offs.
• Production experience in resource-constrained environments.

Responsibilities

• Lead end-to-end development of edge ML models for outdoor monitoring.
• Architect, train, and deploy transformer-based vision models for embedded inference.
• Improve model efficiency via quantization, pruning, and mixed precision.
• Translate product requirements into model targets (accuracy, FPS, memory).
• Collaborate with embedded/firmware teams to integrate models into production pipelines.
• Define evaluation strategies for outdoor edge deployments and robustness.
• Mentor engineers and set best practices for experimentation and deployment.

Skills

Python

PyTorch

C++

Vision transformers

Object detection

Model optimization

Edge deployment

TensorFlow

Education

Bachelor's degree or higher

Tools

TFLite

ONNX Runtime

TensorRT

Job description

SimpliSafe is seeking a Staff Computer Vision Scientist, Edge AI to lead on-device machine learning for outdoor monitoring in real‑time, resource‑constrained environments. You will build and optimize computer vision models for outdoor cameras and doorbells, balancing accuracy with latency and power use in challenging conditions.

You will architect and deploy transformer-based vision models, drive model efficiency, and collaborate with embedded teams to integrate models into production pipelines.