About this role
Samsara is building an AI and machine learning team inside its Operations organization to make the global supply chain AI-native. As one of the early engineers on this team, the successful candidate will design, develop, and deploy advanced machine learning and statistical models that optimize fulfillment, inventory, returns, cash flow, planning, components, suppliers, and cost management. This initiative replaces traditional manual reconciliations, emails, and flat files with modern predictive capabilities using 11 years of operational data, ERP systems, and real-world IoT sensor streams.
This role exists to drive the digital transformation of physical operations by building end-to-end machine learning infrastructure, demand forecasting systems, and supplier risk models. Ideal candidates are deeply technical individual contributors who thrive in ambiguous environments, balance scientific rigor with pragmatic execution, and possess strong cross-functional collaboration skills. Working alongside executive stakeholders and internal business partners, this person will shape the team's modeling standards and technical roadmap while delivering production-grade systems with direct business impact.
What you will do
- Define the end-to-end artificial intelligence transformation roadmap for the supply chain in alignment with company objectives and executive stakeholders.
- Design, train, validate, and deploy machine learning models to production for demand forecasting, inventory optimization, supplier risk scoring, and hardware cash flow.
- Build novel features using large-scale enterprise resource planning, Internet of Things, and third-party datasets while constructing pipelines and extract, transform, load jobs.
- Deliver production-grade code supporting batch and real-time inference following modern machine learning operations best practices.
- Partner with product, engineering, procurement, and finance teams to ensure alignment on data requirements, integration, and operational change management.
- Establish governance frameworks, documentation standards, and quality controls for model development, validation, and lifecycle management.
What you need
- Bring eight or more years of professional experience in applied data science or machine learning, ideally within supply chain, operations research, logistics, or manufacturing.
- Hold a Master's degree or PhD in Computer Science, Statistics, Data Science, Electrical Engineering, Operations Research, or a related technical field.
- Demonstrate expert proficiency in statistical modeling and machine learning techniques including time series forecasting, optimization, anomaly detection, and causal inference.
- Possess strong Python coding skills and fluency in SQL with proven experience developing and deploying production machine learning systems.
- Demonstrate comprehensive proficiency in machine learning operations practices such as automated testing, continuous integration and deployment, model versioning, and performance monitoring.
- Show a track record of building real-time inference pipelines, managing compute resources for training at scale, and utilizing cloud platforms alongside data visualization tools.
Skills and tech stack
Questions candidates ask
Is this position remote?
This is a remote role open to candidates residing in the United States or Canada.
What is the compensation range for this role?
The salary range for this position is $178,640 to $319,000 USD.
What kind of data will I be working with?
You will have access to 11 years of untapped operational data including ERP systems, IoT sensor streams, Salesforce, Slack, Gong call insights, and real-world hardware telemetry.
Who will I partner with across the company?
You will partner directly with Planning, Procurement, Fulfillment, Finance, Sales, and the hardware, firmware, and quality engineering teams.