Senior Machine Learning Engineer - Edge AI

Position Overview

At Samsara—the pioneer of Connected Operations™—you will help shape the future of physical operations. In this remote U.S.-based role, you will work as an experienced Machine Learning Engineer building and shipping AI features that power cutting-edge computer vision, large language models, and multimodal machine learning on edge devices and in the cloud. Your work will make a real-world impact by enhancing safety, efficiency, and sustainability across diverse industries.

Key Responsibilities

• Develop and deploy AI models on edge devices using petabyte-scale data from Samsara’s sensor and camera devices.
• Optimize ML models for real-time inference on edge devices through quantization, sparsification, pruning, and model distillation techniques.
• Collaborate with firmware and hardware teams to integrate ML models into resource-constrained environments for efficient execution.
• Improve edge AI performance by profiling and optimizing latency, memory usage, and energy efficiency across various hardware architectures (CPU, GPU, DSP, NPU).
• Stay updated with the latest research in computer vision and deep learning and apply relevant advancements to product features.
• Work closely with Product Managers to translate customer requirements into scalable ML solutions for real-time video analytics and sensor processing.
• Debug and troubleshoot edge AI deployments, addressing performance bottlenecks, thermal constraints, and reliability issues.
• Champion Samsara’s cultural principles by fostering a collaborative, growth-oriented environment.

Required Qualifications

• 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 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 edge hardware.
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, and ONNX, along with optimization techniques for edge AI.
• 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 by profiling inference performance, reducing latency, and optimizing power efficiency.

Preferred Qualifications

• Ph.D. in Computer Science, Electrical Engineering, or a quantitative discipline (e.g., Applied Math, Physics, or Statistics).
• Experience deploying AI models for real-time processing on edge hardware such as NVIDIA Jetson, Qualcomm Snapdragon, ARM Cortex, or Apple Neural Engine.
• Expertise in large-scale edge ML deployments, including firmware integration and model lifecycle management.
• Experience in DSP optimization for computer vision applications on architectures like Qualcomm Hexagon or ARM NEON.
• Knowledge of power and thermal optimization techniques to balance AI performance with device constraints.

Benefits & Perks

• Compensation: Competitive total compensation package.
• Benefits: Comprehensive health benefits.
• Employee-led remote and flexible working options.
• Opportunities for professional growth and career development.