Staff Machine Learning Engineer - Edge AI

Samsara is a pioneer of the Connected Operations™ Cloud, helping organizations improve the safety, efficiency, and sustainability of their physical operations. As a Staff Machine Learning Engineer, you will lead the design and implementation of AI product initiatives, working with large-scale data to solve critical problems for customers globally.

Responsibilities:

• Lead design and implementation of critical AI product initiatives on Edge devices
• Develop both tactical AI solutions as well as more strategic and longer term research
• Work with petabyte-scale data from customer operations including text, transactions, diagnostics, sensor, camera, and location data
• Partner across business units to explore and prototype new AI experiences and optimize the ML model performance on edge devices
• Stay connected to industry and academic research and adopt novel technology that suits Samsara’s needs
• 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

Requirements:

• 8+ years experience as a Machine Learning Engineer
• Profound experience in optimizing ML models and systems for Edge compute constraints
• Proficient with Spark, Ray, or a similar framework
• Coding in python or similar
• Coding in C++ or Rust
• Strong functional knowledge of the iterative machine learning product development process
• Experienced in developing and shipping production code at large scale
• Ability to distill informal or ambiguous customer and business requirements into crisp problem definitions
• Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge
• Experience coaching and mentoring ML Engineers
• Proficiency in self-serving with data for experiments and model training at scale
• An established record of successful high impact deliveries in AI
• Deep knowledge in state of the art Computer Vision and multi-model models