Senior ML Engineer, Safety AI Backend — Remote

Samsara; Austin (TX); Remote; USD 170,000 - 286,000; Full time

Job summary

Samsara is seeking an experienced Machine Learning Engineer to own the post-training ML backend for Safety AI. You will design production ML APIs, build scalable data pipelines, and productionize artifacts for cloud-scale safety features.

You will collaborate with scientists, firmware, and product teams to ensure reliable, low-latency delivery of insights across millions of devices. The role emphasizes edge-to-cloud integration, model monitoring, and cost-efficient deployment in a

Qualifications

• 6+ years of experience as a Machine Learning Engineer or similar role, with a track record of shipping models in production.
• Strong proficiency in one or more common languages (C++, Golang, Java, Python, Scala).
• Proficiency with ML tools (e.g. Ray/Ray Serve, MLflow, Grafana, Pytorch, Spark) and building data pipelines.

Responsibilities

• Design Production ML APIs: Architect and maintain reliable, low-latency APIs to integrate model outputs into cloud apps.
• Build Data Flywheels: Construct scalable pipelines to power model iteration, backtesting, and online evaluation.
• Optimize & Serve Artifacts: Productionize model artifacts handed off by scientists and tune serving for workloads.
• Petabyte-Scale Operations: Process high-volume camera and sensor data to support model execution and dataset curation.
• Monitor & Maintain Rollout Health: Track model drift, latency, and performance in production.

Skills

C++

Golang

Java

Python

Scala

Education

BS in Computer Science

MS in Computer Science

Tools

Ray

MLflow

Grafana

PyTorch

Spark

Job description

Samsara is seeking an experienced Machine Learning Engineer to own the post-training ML backend for Safety AI. You will design production ML APIs, build scalable data pipelines, and productionize artifacts for cloud-scale safety features.

You will collaborate with scientists, firmware, and product teams to ensure reliable, low-latency delivery of insights across millions of devices. The role emphasizes edge-to-cloud integration, model monitoring, and cost-efficient deployment in a