About this role
Samsara needs resilient cloud-scale backend systems to turn static AI dash camera model artifacts into low-latency safety features running across millions of vehicles. The Safety AI team builds the machine learning and computer vision systems that generate real-time driver alerts and risk signals, and this position owns the cloud-side path from initial integration to rollout monitoring.
Working alongside applied scientists, firmware engineers, and product managers, the person in this role will design production ML APIs, build data flywheels, and manage petabyte-scale sensor telematics data. An ideal candidate brings extensive background in shipping machine learning models to production, a strong grasp of backend software engineering principles, and a focus on reliability, measurement, and safety-critical system performance.
What you will do
- Design and maintain reliable, low-latency APIs to integrate Safety AI model outputs directly into cloud applications.
- Construct scalable data pipelines to power continuous model iteration, backtesting, and online evaluation.
- Productionize model artifacts handed off by applied scientists while optimizing serving logic for platform-specific workloads.
- Process high-volume camera and sensor telematics data to support model execution and automated dataset curation.
- Build monitoring systems to track model drift, precision and recall, and latency regressions in production environments.
- Partner with firmware and platform teams to optimize edge-to-cloud model execution and balance infrastructure costs.
- Collaborate with product managers to translate safety requirements into scalable technical architectures.
What you need
- Bring six or more years of experience working as a machine learning engineer or in a similar capacity with a track record of shipping models to production.
- Demonstrate strong proficiency in one or more common programming languages such as C++, Golang, Java, Python, or Scala.
- Possess hands-on experience with common machine learning tools including Ray or Ray Serve, MLflow, Grafana, PyTorch, and Spark.
- Show practical experience deploying and iteratively refining models using real customer feedback loops.
- Display comfort with full-stack and backend software development by understanding underlying data structures and dependencies.
- Hold a Bachelor of Science or Master of Science degree in Computer Science or a related quantitative field.
Nice to have
- Ph.D. in Computer Science or a quantitative discipline such as Applied Math, Physics, or Statistics.
- Experience with containerization tools like Docker and Kubernetes, CI/CD pipelines, and infrastructure-as-code frameworks.
- Experience deploying and managing machine learning applications in cloud environments including AWS, GCP, or Azure.
- Experience shipping end-to-end machine learning applications in safety-critical or high-scale domains.
- Expertise in optimizing distributed model training utilizing GPUs.
Benefits
- Initial RSU grant with no vesting cliff and ongoing refresh opportunities
- Flexible, employee-led remote model
- Professional development stipend
- Comprehensive health and parental leave plans
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
This is a remote role open to candidates residing in the US or Canada. Samsara embraces a flexible working model that caters to the diverse needs of team members.
What is the salary range for this role?
The range of annual base salary for full-time employees is $170,170 to $286,000 USD. Base pay offered may vary depending on factors including your city of residence, job-related knowledge, skills, and experience.
What tools and technologies will I be working with?
You will use common programming languages like C++, Golang, Java, Python, or Scala alongside machine learning tools such as Ray, MLflow, Grafana, PyTorch, and Spark. The stack also involves containerization, cloud environments, and petabyte-scale data processing pipelines.
What kind of team will I be collaborating with?
You will partner closely with applied scientists, firmware and full-stack engineers, and product managers. The role sits within the Safety AI team, which builds the machine learning and computer vision systems behind AI dash cameras.