Lead Machine Learning Engineer- ML Infrastructure

Details

• You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
• You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world's physical operations.
• You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.
• You believe customers are more than a number: Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.
• You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together.
• 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.
• 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.
• 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.
• Own reliability, observability, and security for ML systems across cloud and edge, including on-call practices, incident response, and infrastructure hardening.
• 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 and role model Samsara’s cultural principles: Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team.
• 10+ years in machine learning engineering, with demonstrated tech lead ownership of at least two major ML platform domains (distributed training, data/research infrastructure, cloud inference, or feature engineering) serving multiple product teams at scale.
• Proven record of shipping ML-powered features end-to-end — from design through production and iteration — with measurable impact on product or business metrics (not just building internal tooling).
• Hands-on Ray and Kubernetes expertise in production environments; Spark experience strongly preferred. Able to be a credible peer to the most senior engineers on the team.
• Deep understanding of ML fundamentals beyond pipelines: evaluation methodology, dataset design, ablation, drift, and the ability to review and redirect modeling approaches — you bridge research and engineering, not just serve them.
• Demonstrated cross-org technical leadership around platform decisions, and influencing roadmap and go/no-go calls based on throughput, latency, and cost trade-offs.
• Experience navigating science-engineering tension — knowing when to hold the platform line and when to adapt for research velocity, and communicating that clearly to both sides.
• Prior contributions to open source projects (Ray, Spark, RayDP, or Kubernetes).
• Experience with enterprise security/compliance in ML environments.
• Background working with edge/on-device ML and firmware/embedded teams.