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Samsara (NYSE: IOT) is the pioneer of the Connected Operations Cloud, a platform enabling organizations to harness IoT data from physical operations—agriculture, construction, field services, transportation, and manufacturing—to improve safety, efficiency, and sustainability.
This is the most senior individual contributor role on Samsara's Safety AI team, focused on taking computer vision models from research to production in safety-critical systems. The role sits at the intersection of cutting-edge CV research and production systems running across millions of IoT devices globally. Models run on the edge in real time on trucks, vans, and heavy equipment.
Key scope: Millions of AI dash cams, 100B+ miles/year of data (~99% of U.S. roads), 25T+ data points. Problems include driver behavior understanding, 3D scene reasoning, and long-tail event detection in real-world environments. The technical stack involves modern vision models (transformers), large-scale training pipelines, and optimization for edge inference on constrained devices.
Responsibilities:
- Architect end-to-end computer vision pipelines for real-world safety detection (object detection, tracking, semantic segmentation, multi-camera fusion)
- Drive the technical roadmap for edge and cloud perception, including model optimization and deployment on constrained hardware
- Partner closely with hardware and firmware teams to co-design the full stack
- Work with petabyte-scale multimodal data (video, sensor, telematics, diagnostics) to train and iterate on production models
- Stay at the frontier of CV and perception research and translate insights into shipped product
- Mentor and technically guide senior scientists and engineers across the team
- Translate customer and business needs into precise, solvable engineering challenges
Samsara's AI has helped prevent accidents that would have harmed an estimated 380,000 people per year. This role offers the autonomy and support to make long-term impact as part of a recently public company in a hyper-growth environment.
