Machine Learning Engineer

Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. Samsara is helping improve the safety, efficiency and sustainability of the physical operations that power our global economy.

Samsara is building an AI/ML team inside its Operations team to make our supply chain AI native. You would be one of its first AI/ML engineers. You'll design, develop, and deploy advanced machine learning and statistical models that optimize our global supply chain. From forecasting demand to modeling supply risk using real-time IoT signals, you'll apply cutting-edge techniques to solve complex problems with direct business impact.

• Define the end-to-end AI transformation roadmap for supply chain alongside the Senior Director of Supply Chain AI Transformation, aligning with company OKRs and executive stakeholders
• Design, train, validate, and deploy ML models to production (demand forecasting, inventory optimization, supplier risk scoring, cellular spend prediction, HW cash flow), ensuring robust MLOps practices and measurable ROI
• Build predictive models to forecast demand, inventory, lead times, and spend across Samsara's global supply network
• Create novel features using large-scale ERP, IoT, and third-party datasets
• Build demand, inventory and cost forecasting systems for highly variable, seasonal SKUs with intermittent patterns and long lead times
• Model supplier risk using real-time IoT telemetry, geopolitical signals, and market data
• Design cost optimization algorithms balancing inventory carrying costs, expedite fees, and revenue risk across multiple regions
• Create anomaly detection systems for cellular connectivity spend and supply chain disruptions using multimodal data

• Motivated by impact: your work will drive smarter, faster, more sustainable supply chain decisions across Samsara's operations
• Combine scientific rigor with real-world pragmatism: skilled at navigating trade-offs between model complexity, performance, and maintainability
• Want to work on hard problems: forecasting volatility, cost optimization, anomaly detection, and more using large, noisy, high-dimensional datasets
• Self-directed leader: identify analytical white space, set multi-quarter plans, and mentor others through influence
• Enjoy building models that matter: solutions will be deployed in production, drive decisions, and be regularly evaluated against business KPIs
• Seed AI/ML capabilities and enable the broader organization: establish foundations, set standards, and empower cross-functional teams to adopt data-driven decision making

• Remote role open to candidates residing in the US or Canada
• Access to 10+ trillion data points annually from connected operations
• Work with ERP data, IoT sensor streams, third-party market intelligence, and operational metrics