Senior Machine Learning Engineer, Edge AI

About the role:

Samsara is looking for an experienced Machine Learning Engineer for our Machine Learning Engineering team. Our Machine Learning team is responsible for leading the teams that build and ship Samsara’s AI features, and the infrastructure that supports them. This includes computer vision, large language models, and multimodal machine learning on edge devices and in the cloud to support our rapidly growing footprint of customers. This organization is building the ML foundation to stay ahead of the needs of our expanding customer base and platform. 

This is a remote position open to candidates based in the United States and Canada. 

You should apply if:

• You want to impact the industries that run our world: Your efforts will result in real-world impact—helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.
• You are the architect of your own career: If you put in the work, this role won’t be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.
• You’re energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.
• You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high-caliber team that will encourage you to do your best. 

Click here to learn about what we value at Samsara. 

In this role, you will: 

• 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.
• Champion Samsara’s cultural principles, fostering a collaborative and growth-oriented team environment.
• Champion, role model, and embed Samsara’s cultural principles (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

Minimum requirements for the role:

• 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 (e.g., 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.

An ideal candidate also has:

• Ph.D. in Computer Science, Electrical Engineering, or a quantitative discipline (e.g., Applied Math, Physics, 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 Qualcomm Hexagon, ARM NEON, or similar architectures.
• Knowledge of power and thermal optimization techniques to balance AI performance with device constraints in edge computing environments.