Lead ML Infrastructure Engineer — Remote

Aimlroles; Northern (KY); Hybrid; USD 200,000 - 358,000; Full time

Job summary

Samsara is hiring a Lead Machine Learning Infrastructure Engineer to own the end-to-end ML platform across Safety AI. You will shape architecture for training, inference, and edge deployment across 2M+ devices, partnering with security and data platforms while guiding multiple product teams.

This senior role requires 10+ years in ML engineering, hands-on Ray/Kubernetes/Spark experience, and leadership to drive platform decisions and a strong developer experience.

Qualifications

• 10+ years in machine learning engineering with tech lead ownership of multiple ML platform domains.
• Proven track record shipping ML features end-to-end with measurable impact.
• Hands-on experience with Ray, Kubernetes and Spark in production environments.
• Cross-organ technical leadership and ability to influence roadmaps and go/no-go decisions.
• Experience balancing research velocity with platform stability and security considerations.

Responsibilities

• Set the technical strategy and deliver Samsara's ML platform (training, inference, edge) across multiple Safety AI product teams.
• Drive design, launch, and iteration of Safety AI features including CV models and edge deployment.
• Design and operate scalable online and batch inference systems (Ray, Spark) with observability and SLOs.
• Own reliability, observability, and security for ML systems across cloud and edge, including on-call practices.
• Be the technical authority for ML infrastructure architecture and mentor senior engineers.

Skills

Machine learning engineering

Technical leadership

Ray

Kubernetes

Spark

Platform ownership

Tools

Ray

Spark

Kubernetes

Job description

Samsara is hiring a Lead Machine Learning Infrastructure Engineer to own the end-to-end ML platform across Safety AI. You will shape architecture for training, inference, and edge deployment across 2M+ devices, partnering with security and data platforms while guiding multiple product teams.

This senior role requires 10+ years in ML engineering, hands-on Ray/Kubernetes/Spark experience, and leadership to drive platform decisions and a strong developer experience.