Senior Machine Learning Engineer - Platform

Samsara

Location not specified

Full Time

Posted April 23, 2026

CA$157k - CA$204k

Not Specified

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Job Description

The role involves leading the development and architecture of safety-critical machine learning systems for IoT devices, focusing on building scalable, reliable, and explainable perception platforms that operate across cloud and edge environments to improve physical operations and safety.

Key Responsibilities

Architect a unified perception platform supporting reusable components and downstream safety applications.
Design and implement real-time machine learning systems from sensor ingestion to risk reasoning and actuation.
Manage model deployment across edge and cloud environments, including versioning, rollouts, and fallback mechanisms.
Own end-to-end latency and reliability for safety-critical data pipelines.
Develop monitoring systems to detect model drift, false positives/negatives, and latency issues, and incorporate feedback into training.
Create evaluation frameworks for rare safety events, defining metrics and test sets for safety validation.
Collaborate with applied scientists to translate research into production code that is performant, debuggable, and explainable.
Shape system abstractions to support platform scalability and minimize technical debt.
Mentor engineering team members to uphold standards of correctness and performance.

Requirements

Minimum of 6 years of experience in ML Engineering, with a proven track record of shipping models in production, ideally in safety-critical domains like robotics, automotive, or industrial AI.
Deep understanding of distributed systems, performance profiling, and computer vision.
Experience with Cloud ML workflows on AWS, GCP, or Azure, and containerization technologies such as Docker and Kubernetes.
Experience with containerization technologies e.g., Docker, Kubernetes, continuous integration/continuous deployment (CI/CD) pipelines, and infrastructure-as-code (IaC) frameworks.
Familiarity with deploying and managing ML applications in cloud environments, leveraging cloud-based services for data storage, processing, and inference.
Experience building end-to-end ML applications from scratch.
Ability to design systems, understanding trade-offs between model complexity and operational reliability.
Educational background with a Ph.D. in Computer Science or a quantitative discipline such as Applied Math, Physics, or Statistics (preferred but not explicitly mandatory).