Job Description
The role involves developing, deploying, and optimizing AI models on edge devices for real-time video analytics and sensor processing, collaborating with hardware teams, and applying advanced machine learning techniques to improve edge AI performance in resource-constrained environments.
Key Responsibilities
- Develop and deploy AI models on edge devices using large-scale sensor and camera data.
- Optimize machine learning models for real-time inference by applying techniques such as quantization, pruning, sparsification, and model distillation.
- Collaborate with firmware and hardware teams to integrate ML models into resource-constrained environments.
- Profile and optimize AI performance across different hardware architectures to improve latency, memory usage, and energy efficiency.
- Stay current with research in computer vision, deep learning, and embedded AI, applying advancements to products.
- Translate customer requirements into scalable ML solutions for real-time video analytics and sensor processing.
- Troubleshoot and debug edge AI deployments, addressing performance, thermal, and reliability issues.
Requirements
- Develop and deploy AI models on edge devices by working with petabyte-scale data from Samsara s camera and sensor devices.
- Optimize ML models for real-time inference on edge devices by implementing quantization, sparsification, pruning, and model distillation techniques.
- Collaborate with firmware and hardware teams to integrate ML models into resource-constrained environments, ensuring efficient execution.
- Improve edge AI performance by profiling and optimizing latency, memory usage, and energy efficiency across different hardware architectures CPU, GPU, DSP, NPU.
- Stay up to date with the latest research in computer vision, deep learning, and embedded AI, applying relevant advancements to Samsara s products.
- Work closely with Product Managers to translate customer requirements into scalable and efficient ML solutions for real-time video analytics and sensor processing.
- Debug and troubleshoot edge AI deployments, addressing performance bottlenecks, thermal constraints, and reliability issues in production environments.
- Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team as we scale globally and across new offices.
- BS or MS in Computer Science, Electrical Engineering, or a related field with a focus on ML or embedded systems.
- 5 years of experience in embedded machine learning or a similar role.
- 4 years of experience in deploying machine learning models in embedded systems.
- Proficiency in embedded systems programming, including low-level optimization for inference workloads.
- Strong coding skills in C, Golang, or Python, with experience optimizing ML models for deployment on edge hardware.
- Hands-on experience with ML frameworks like PyTorch, TensorFlow, ONNX, and optimization techniques for edge AI such as quantization, pruning, sparsification.
- Experience in computer vision and media processing on edge mobile devices, including real-time object detection, tracking, and scene analysis.
- Proven ability to troubleshoot and debug edge AI systems, including profiling inference performance, reducing latency, and optimizing power efficiency.
Benefits & Perks
Competitive total compensation package
Employee-led remote and flexible working
Health benefits
Samsara for Good charity fund
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