The opportunity
Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are…
What you'll do
You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world's physical operations.
You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.
You believe customers are more than a number: Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.
You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together.
Set the technical strategy and own end-to-end delivery of Samsara's ML: platform (training, experimentation, batch/online inference, edge) — making architectural decisions and being the accountability point across all platform layers for multiple Safety AI product teams.
What they're looking for
- Drive the design, launch, and iteration of Safety AI features (CV models,: EcoDriving insights, LLM-based reporting) — not just enabling others to ship, but co-owning outcomes including safety metrics, reliability, and cost at production scale.
- Design and operate scalable online and batch inference systems (Ray, Spark),: including deployment patterns, observability, SLOs, and unified training-to-production workflows.
- Partner with firmware and edge teams to package, validate, and deploy models: to Samsara devices , and build feedback loops from edge to cloud for continuous improvement.
- Own reliability, observability, and security for ML systems across cloud and: edge, including on-call practices, incident response, and infrastructure hardening.