Sr. Software Engineer, Embedded AI
Role details
Job location
Tech stack
Job description
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.
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Build multi-modal pipelines and features that integrate vision, audio, radar, text, and other inputs for high-accuracy AI customer experiences.
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Optimize and deploy AI model applications for constrained environments, including benchmarking on hardware.
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Collaborate cross-functionally with cloud, mobile, QA, product, UX, and hardware teams to ship AI-powered experiences at scale.
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Serve as a technical mentor and system owner, influencing team strategy, reviews, and roadmap prioritization.
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Develop tools and frameworks to support model evaluation, A/B testing, and automated performance monitoring across both cloud and edge environments.
Requirements
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Bachelor's or Master's in Computer Engineering, Computer Science, Electrical Engineering, or similar
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5+ years of hands-on experience in embedded software and/or applied machine learning in production
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Proven ability to design and deploy real-time systems on embedded Linux (or RTOS)
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Highly Proficient in C++, Rust, and Python in production environments
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Experience with AI model lifecycle: training, conversion (ONNX, TensorRT, TFLite), quantization, and pruning
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Knowledge of cloud platforms (GCP, AWS, Azure) and edge-cloud coordination
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Solid understanding of system-level design, debugging, and performance tuning
Preferred Qualifications
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Computer Vision & ML: Classification, Detection, Tracking, Recognition, LLM/VLM integration, Pose Estimation, Vector Embeddings
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Multi-modal ML and Sensor Fusion: visual, audio, radar, and text data
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Model Optimization: Post-training quantization, pruning, distillation, benchmarking on NPUs/DSPs/ASICs
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Media & Signal Processing: GStreamer, FFmpeg, MediaPipe, OpenCV
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Communication Protocols: MQTT, gRPC, Bluetooth, Wi-Fi, WebRTC
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DevOps: CI/CD (GitLab), versioning, monitoring
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Containerization: Docker, Kubernetes
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Security & Privacy: Secure boot, data encryption, firmware signing
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Databases: Vector DBs, Time-Series, Graph-based Knowledge Systems
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Collaboration Tools: JIRA, Confluence, Slack, Teams