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 cloud-side path from model artifacts to production systems for Safety AI. You will build ML APIs, data pipelines, and evaluation infrastructure to run models at fleet scale, partnering with scientists and engineers to ensure reliable, low-latency, real-time safety signals.

The role emphasizes production deployment, continuous iteration, and collaboration across teams, with a remote-friendly model in the US and Canada.

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 languages (e.g., C++, Golang, Java, Python, Scala).
• Experience deploying and iteratively refining models using real customer feedback loops.
• Comfort with full-stack/backend development and understanding of data structures underneath models.

Responsibilities

• Design Production ML APIs: Architect and maintain reliable, low-latency APIs to integrate model outputs into cloud apps.
• Build Data Flywheels: Create scalable pipelines for continuous model iteration and safe deployments.
• Optimize & Serve Artifacts: Productionize model artifacts and fine-tune for workloads.
• Petabyte-Scale Operations: Process high-volume camera and sensor data to support model execution.
• Monitor & Maintain Rollout Health: Track drift, precision/recall, latency in production.
• Cross-Functional System Integration: Collaborate with firmware and platform teams for edge-to-cloud execution.

Skills

C++

Golang

Java

Python

Scala

Education

BS or MS in Computer Science

Ph.D. in Computer Science (nice to have)

Tools

Docker

Kubernetes

Ray Serve

MLflow

Grafana

PyTorch

Spark

Job description

Samsara is seeking an experienced Machine Learning Engineer to own the cloud-side path from model artifacts to production systems for Safety AI. You will build ML APIs, data pipelines, and evaluation infrastructure to run models at fleet scale, partnering with scientists and engineers to ensure reliable, low-latency, real-time safety signals.

The role emphasizes production deployment, continuous iteration, and collaboration across teams, with a remote-friendly model in the US and Canada.