Senior Machine Learning Engineer

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, enhancing driver safety and engagement through innovative in-vehicle experiences.

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