About this role
About the Role Samsara is the industry leader in AI for physical operations. We're hiring a Lead Machine Learning Infrastructure Engineer to serve as the technical anchor for ML infrastructure across Samsara's Safety AI organization. You will own the architecture and evolution of our end-to-end ML platform — spanning training, experimentation, inference, and edge deployment across more than 2M deployed devices — and be the connective tissue between applied ML teams, security, and data platform. This role is not an execution role sitting under a technical lead; you are the technical lead. Your decisions shape platform direction, unblock multiple product teams, and translate directly into real-world safety outcomes for the industries that run our world. We are open to calibrating this role at Staff, Senior Staff, or Principal level depending on the candidate's scope of experience — what matters most is end-to-end platform ownership and the ability to operate as the technical anchor for the organization. This is a remote position open to candidates based in the United States. What You'll Do
- ML Platform & Infrastructure: Set the technical strategy and own end-to-end delivery of Samsara's ML platform (training, experimentation, batch/online inference, edge) — making architectural decisions and being the accountability point across all platform layers for multiple Safety AI product teams.
- Experimentation & Measurement: Drive the design, launch, and iteration of Safety AI features (CV models, EcoDriving insights, LLM-based reporting) — not just enabling others to ship, but co-owning outcomes including safety metrics, reliability, and cost at production scale.
- Inference & Edge Deployment: Design and operate scalable online and batch inference systems (Ray, Spark), including 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 feedback loops from edge to cloud for continuous improvement.
- Reliability, Security & Operations: Own reliability, observability, and security for ML systems across cloud and edge, including on-call practices, incident response, and infrastructure hardening.
- Ownership & Leadership: Own or co-own end-to-end technical delivery for high-priority or high-risk initiatives, from modeling and system design through production rollout. Be the technical authority for ML infrastructure architecture across Safety AI — setting direction that cross-functional teams (applied ML, firmware, security, data platform) execute against, mentoring senior engineers and applied scientists, and ensuring platform decisions are made at the right level of abstraction with the right trade-offs. Drive strong developer experience through documentation and best practices, while contributing to and representing Samsara in open source communities (Ray, Spark, RayDP).
- Champion Samsara’s culture: Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team. What We're Looking For
- 10+ years in machine learning engineering with demonstrated tech lead ownership of at least two major ML platform domains (distributed training, data/research infrastructure).
- Experience designing and delivering end-to-end ML platforms, including training, experimentation, inference, and edge deployment.
- Strong collaboration skills with cross-functional teams (applied ML, firmware, security, data platform) and a track record of mentoring engineers.
- Familiarity with open source ML tooling and ecosystems; contributions to communities like Ray, Spark, or RayDP are a plus.
- Ability to operate as the technical anchor for the organization, making platform-level trade-offs and setting direction for multiple product teams. Nice to Have
- Experience with Ray, Spark, or RayDP; open-source contributions.
- Prior exposure to Safety AI or industrial IoT contexts.
- Track record of building scalable, secure ML systems deployed to edge devices.