CORP - Staff Software Engineer

Location: Lehi, UT 84043 Hybrid Schedule : (Onsite on Monday, Tuesday, Wednesday, Thursday ) Pay Rate: $80 - $85/hr on w2 About This Role We are seeking a Staff Software Engineer, AI/ML to lead the development of advanced AI applications running on embedded devices and cloud infrastructure across our smart home ecosystem. This role bridges cutting-edge AI/ML models with fully integrated smart home security devices. As a technical leader, you'll drive efforts across on-device AI, multi-modal sensor fusion, and cloud-edge coordination, working closely with cross-functional teams. • Build multi-modal pipelines and features that integrate vision, audio, radar, text, and other inputs for high-accuracy AI customer experiences. • Optimize and deploy AI model applications for constrained environments, including benchmarking on hardware. • Collaborate cross-functionally with cloud, mobile, QA, product, UX, and hardware teams to ship AI-powered experiences at scale. • Serve as a technical mentor and system owner, influencing team strategy, reviews, and roadmap prioritization. • Develop tools and frameworks to support model evaluation, A/B testing, and automated performance monitoring across both cloud and edge environments. Required Qualifications • Bachelor's or Master's in Computer Engineering, Computer Science, Electrical Engineering, or similar • 5+ years of hands-on experience in embedded software and/or applied machine learning in production • Proven ability to design and deploy real-time systems on embedded Linux (or RTOS) • Highly Proficient in C++, Rust, and Python in production environments • Experience with AI model lifecycle: training, conversion (ONNX, TensorRT, TFLite), quantization, and pruning • Knowledge of cloud platforms (GCP, AWS, Azure) and edge-cloud coordination • Solid understanding of system-level design, debugging, and performance tuning Preferred Qualifications • Computer Vision & ML: Classification, Detection, Tracking, Recognition, LLM/VLM integration, Pose Estimation, Vector Embeddings • Multi-modal ML and Sensor Fusion: visual, audio, radar, and text data • Model Optimization: Post-training quantization, pruning, distillation, benchmarking on NPUs/DSPs/ASICs • Media & Signal Processing: GStreamer, FFmpeg, MediaPipe, OpenCV • Communication Protocols: MQTT, gRPC, Bluetooth, Wi-Fi, WebRTC • DevOps: CI/CD (GitLab), versioning, monitoring • Containerization: Docker, Kubernetes • Security & Privacy: Secure boot, data encryption, firmware signing • Databases: Vector DBs, Time-Series, Graph-based Knowledge Systems • Collaboration Tools: JIRA, Confluence, Slack, Teams

Created: 2026-03-10