CORP - Staff Software Engineer

2 weeks ago

Lehi, UT, United States LanceSoft Full time
Job Description

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

Meet Your Recruiter

Danish Qureshi

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