Lead Machine Learning Engineer - ML Infrastructure

Samsara is the pioneer of the Connected Operations Cloud, helping organizations that depend on physical operations improve safety, efficiency, and sustainability through IoT data. The Lead Machine Learning Infrastructure Engineer will serve as the technical anchor for ML infrastructure across the Safety AI organization, owning the architecture and evolution of the end-to-end ML platform across training, experimentation, inference, and edge deployment. The role also involves leading ML feature delivery, ensuring reliability and security, partnering across engineering teams, and mentoring senior technical staff.

Responsibilities:

• 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

Requirements:

• This is a remote position open to candidates based in the United States
• 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
• All offers of employment are contingent upon an individual's ability to secure and maintain the legal right to work at the company and in the specified work location, if applicable
• Spark experience strongly preferred
• 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