Lead Machine Learning Engineer - ML Infrastructure
Technologies
Ray · Spark · AWS · Kubernetes · computer vision · LLM · edge ML · model registry · RayDP
About Samsara
Connects fleets, equipment, sites, and frontline teams through IoT devices, software workflows, and AI for physical operations.
Public · 2000–5000 people
Job description
The full responsibilities and requirements are on the employer’s site.
Read the job description ↗Source and classification
Internal deployment & tooling · Evidence for this classification:
Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term. About the role: Samsara is the industry leader in AI for physical operations. We're hiring a Lead Machine Learning Infrastructure Engineer to serve as the technical anchor for ML infrastructure across Samsara's Safety AI organization. You will own the architecture and evolution of our end-to-end ML platform — spanning training, experimentation, inference, and edge deployment across more than 2M deployed devices — and be the connective tissue between applied ML teams, security, and data platform. This role is not an execution role sitting under a technical lead; you are the technical lead. Your decisions shape platform direction, unblock multiple product teams, and translate directly into real-world safety outcomes for the industries that
More from the job description
Who we are 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 helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale. Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term. About the role: Samsara is the industry leader in AI for physical operations. We're hiring a Lead Machine Learning Infrastructure Engineer to serve as the technical anchor for ML infrastructure across Samsara's Safety AI organization. You will own the architecture and evolution of our end-to-end ML platform — spanning training, experimentation, inference, [... source excerpt omitted ...] rkers return home safely. 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’r [... source excerpt omitted ...] ety 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. Inference & Edge Deployment 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. Reliability, Security & Operations Own reliability, observability, and security for ML systems across cloud and edg
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