Senior Machine Learning Engineer

Samsara

full-time

Posted on: 9/5/2025

Location: 🇺🇸 United States

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💰 $135,482 - $227,700 per year

Senior

CloudDockerGoIoTJavaKubernetesPythonPyTorchRayScalaSparkTensorflow

About the role

Design and implement scalable machine learning infrastructure using Ray to support model training, deployment, and inference at scale.
Leverage Kubernetes for orchestration of containerized applications, ensuring seamless deployment, scaling, and management of ML models and associated services.
Develop and maintain CI/CD pipelines for automated testing, deployment, and management of ML applications and infrastructure.
Implement robust monitoring, logging, and alerting systems to ensure high availability, performance, and security of the ML platform.
Collaborate with data scientists and ML engineers to optimize data pipelines and model performance.
Provide DevOps/SRE support for the ML platform, including incident response, performance tuning, and disaster recovery planning.
Stay abreast of the latest advancements in machine learning technologies and infrastructure, and advocate for adoption of best practices and new technologies within the team.
Work closely with various engineering teams across ML, full-stack, firmware as well as cross functional partners to deliver core infrastructure, services, and optimizations.
Champion and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team).

BS or MS in Computer Science or other relevant field.
6+ years of experience as a Machine Learning Engineer, Applied Scientist, or similar role.
Strong proficiency in one or more common languages (e.g., C++, Golang, Java, Python, Scala).
Proficiency with common ML tools (e.g., Spark, TensorFlow, PyTorch).
Experience deploying and iteratively refining models using customer feedback loops.
Comfortable with full-stack / backend development code to build a strong understanding of underlying data structures and other dependencies.
This is a remote position open to candidates residing in the US.
(Preferred) Ph.D. in Computer Science or quantitative discipline (e.g., Applied Math, Physics, Statistics).
(Preferred) Experience building, deploying, and optimizing ML models on the edge.
(Preferred) Experience building end-to-end ML applications from scratch.
(Preferred) Expertise in optimizing distributed model training with GPUs.