• Design and implement backend services that ingest, normalize, and persist high-volume telemetry using event-driven and MQTT-based pipelines. • Implement secure authentication and bi-directional communication for commands and diagnostics, designed for intermittent connectivity. • Integrate edge AI outputs (range prediction, health monitoring) into cloud services and surface them explainably in a Fleet Portal UI. • Architect robust MQTT communication protocols for data exchange between the eAPU and various external devices/cloud services. • Act as the technical "right-hand" to the leadership team, making pragmatic trade-offs between speed, reliability, and simplicity.

• Own the full ML lifecycle: Take projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation. • Translate business needs into ML solutions: Gather product requirements and translate them into robust ML system design requirements. • Build recommendation and ranking systems: Architect and launch ranking and recommendation infrastructure from scratch, initially via integrated off-the-shelf models, and evolving to targeted and customized solutions in the long term. • Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data. • Collaborate cross-functionally: Partner with cross-functional (product, infra, data engineering, and software engineering) teams to build robust, high-scale systems that underlie all of our data processing and ML Operations.

• Design and build distributed systems that process, enrich, and respond to billions of behavioral events per day in real time • Develop high-performance APIs and services that support advertising, identity, and intent features across the Marketing Platform • Leverage machine learning and large language models (LLMs) to analyze behavioral data, classify content, extract signals, and enable intelligent decision-making • Build intelligent agents using frameworks like LangGraph or MCP to reason over data and power user-facing insights • Design and operate data pipelines using tools like Kafka, Kinesis, and ClickHouse to support both streaming and batch workloads • Drive quality, performance, scalability, and observability across all systems you own • Collaborate cross-functionally with product managers, data scientists, and engineers to deliver customer-facing features and internal tooling • Contribute to technical leadership and mentorship of teammates.

• Build and evolve core AI platform capabilities that enable teams to develop, run, and scale GenAI-powered applications across Samsara • Design and implement shared execution patterns, APIs, and services that support multi-step AI workflows and system integrations • Develop reliable, extensible backend systems that power AI-driven experiences used across the sales funnel and beyond • Work hands-on across the stack, from backend services and execution infrastructure to integration with AI models and tooling • Collaborate closely with AI engineers, data scientists, product partners, and sales operators to turn emerging AI use cases into production-ready platform capabilities