Samsara is hiring a remote Lead ML Engineer ML Infrastructure!
Samsara, a publicly traded company behind the Connected Operations Cloud for IoT-driven physical operations, is hiring a Lead Machine Learning Infrastructure Engineer to act as the technical anchor for ML infrastructure within its Safety AI organization. The role is explicitly a technical leadership position rather than an execution role beneath a lead: the person in it sets platform architecture and is the accountability point across training, experimentation, batch and online inference, and edge deployment for a fleet of more than two million connected devices. The platform work supports computer-vision safety models, EcoDriving insights, and LLM-based reporting, and the role sits at the junction of applied ML teams, security, firmware, and the data platform group. Samsara is open to calibrating the title at Staff, Senior Staff, or Principal depending on the candidate's scope, and the position is remote for candidates based in the United States.
Key responsibilities
- Own the technical strategy and end-to-end delivery of the ML platform across training, experimentation, batch and online inference, and edge deployment.
- Make architectural decisions spanning all platform layers and serve as the accountability point for multiple Safety AI product teams.
- Co-own product outcomes for Safety AI features, including safety metrics, reliability, and production cost at scale.
- Design and operate scalable online and batch inference systems using Ray and Spark, with deployment patterns, observability, SLOs, and unified training-to-production workflows.
- Partner with firmware and edge teams to package, validate, and deploy models to Samsara devices, and build edge-to-cloud feedback loops.
- Own reliability, observability, and security for ML systems across cloud and edge, including on-call practices and incident response.
- Set ML infrastructure architecture direction across the organization, mentor senior engineers and applied scientists, and guide trade-offs around throughput, latency, and cost.
- Improve developer experience through documentation and best practices, and contribute to open source communities such as Ray, Spark, and RayDP.
Key requirements
- 10+ years in machine learning engineering, with tech lead ownership of at least two major ML platform domains serving multiple product teams at scale.
- A track record of shipping ML-powered features end to end, from design through production iteration, with measurable product or business impact.
- Hands-on Ray and Kubernetes expertise in production; Spark experience strongly preferred.
- Depth in ML fundamentals beyond pipelines: evaluation methodology, dataset design, ablation, drift, and the ability to review and redirect modeling approaches.
- Demonstrated cross-organizational technical leadership, including influencing roadmaps and go/no-go decisions.
- Experience navigating science-engineering tension and communicating platform trade-offs to both research and engineering audiences.
- Nice to have: open source contributions to Ray, Spark, RayDP, or Kubernetes; enterprise security and compliance experience in ML; edge or on-device ML background.
- Seniority: Lead / Principal (calibrated at Staff, Senior Staff, or Principal)
- Work mode: Remote (US-based candidates)
- Salary: $196,000 – $269,500 CAD annual base
- Key skills: Ray, Kubernetes, Apache Spark, RayDP, distributed training, online and batch inference, edge/on-device ML deployment, observability and SLOs, ML evaluation methodology, open source contribution
Want more jobs?
Join our mailing list to receive updates about new data science and Lead ML Engineer ML Infrastructure jobs.
