Note: The job is a remote job and is open to candidates in USA. Samsara is a leader in AI for physical operations and develops connected operations solutions for industries such as transportation, construction, and manufacturing. The Lead Machine Learning Infrastructure Engineer will serve as the technical anchor for 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 includes leading ML feature delivery, reliability and security, cross-functional technical direction, and mentorship.
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
Skills
- 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
- 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
- This is a remote position open to candidates based in the United States
- 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
Benefits
- This role is also eligible for an initial RSU grant with no vesting cliff, and ongoing refresh opportunities tied to performance, subject to plan terms and conditions.
- Performance-based bonus/variable pay
- Equity (for eligible roles)
- A flexible, employee-led remote model
- A professional development stipend
- Comprehensive health and parental leave plans
- Remote work where it aligns with our operational requirements
Company Overview