Senior Machine Learning Engineer - Edge AI

Samsara is the pioneer of the Connected Operations™ Cloud, enabling organizations to harness IoT data for actionable insights. The Senior Machine Learning Engineer will develop and optimize ML models for embedded environments, directly impacting driver safety and engagement.

Responsibilities:

• Design, optimize, and deploy computer vision and multimodal ML models that run efficiently on constrained edge platforms powering Samsara’s in-vehicle camera systems
• Apply advanced model optimization techniques—such as quantization, pruning, and distillation—to achieve real-time inference under strict CPU, memory, and thermal constraints
• Partner with ML research and product teams to translate new AI detections into deployable, maintainable edge models
• Collaborate with firmware, ML research, and hardware teams to productize our ML runtime pipeline, bringing scalable, reliable, and testable on-device inference to production
• Develop performance benchmarking, profiling, and validation frameworks for edge-deployed models to ensure robustness across millions of deployed devices
• Drive continuous improvement of our edge ML toolchain and advocate for best practices in model optimization, inference reliability, and deployment efficiency
• Mentor peers on efficient inference design and collaborate cross-functionally to accelerate feature delivery for safety and driver experience programs
• 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:

• 5+ years of experience developing and deploying deep learning models for edge, embedded, or real-time systems
• Strong background in computer vision or multimodal ML (e.g., 2D/3D CNNs, Transformers) using industry-standard deep learning frameworks
• Proficiency in Python and C++, with hands-on experience optimizing inference runtimes and applying model optimization techniques for edge deployment
• Deep understanding of performance tuning, including compiler- or DSP-level optimizations, runtime profiling, latency analysis, and memory management on constrained hardware
• Familiarity with middleware or streaming frameworks used in real-time perception pipelines
• Excellent cross-functional communication and collaboration skills, especially across ML, firmware, and product domains
• Experience bringing ML infrastructure or runtime systems from prototype to production at scale
• Background in multimodal ML (e.g., audio + vision fusion) or event-based detection systems
• Experience validating AI models across large, diverse fleets of deployed devices in real-world environments