Staff MLE - Supply Chain

About the role:

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.

We design connected hardware (cameras, vehicle gateways, sensors), build it with joint development manufacturing partners, and deliver it to tens of thousands of customers in industries that keep the economy running. Every link in that chain often runs on emails, flat files and manual reconciliations. We are moving our internal operations into the future with modern AI capabilities.

We expect you to work across the supply chain including fulfillment, inventory, returns, cash, planning, components, suppliers and cost management. You will partner directly with Planning, Procurement, Fulfillment, Finance, Sales, and the hardware, firmware and quality engineering teams.

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.

What makes this unique:

• 11 years of untapped operational data: ERP systems (e.g. NetSuite, e2open, Propel), IoT sensor streams, Salesforce, Slack, Gong call insights, and real-world hardware telemetry
• Own the full stack: Modeling, MLOps, deployment, monitoring—build production systems that drive real business decisions
• Real hardware supply chain: Complex multi-tier supplier networks, lead times, high-stakes NPI ramps
• Direct executive partnership: Collaborating cross-functionally with teams in Product, Engineering, Procurement, and Finance

In this role you will:

• 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 pipelines and ETL jobs to serve models and stakeholders
• Deliver production-grade code that supports both batch and real-time inference with MLOps best practices
• Act as the AI liaison to Product, Engineering, Procurement, and Finance—ensuring alignment on data requirements, integration, and change management
• Drive enhancements to our data infrastructure and analytics platform to support real-time model training, monitoring, and inference at scale
• Mentor junior scientists through code reviews and collaborative project work
• Act as a key scientific voice in roadmap planning, experimentation frameworks, and modeling strategy discussions
• Identify gaps in data, tools, and processes—and lead initiatives to close them
• Establish governance frameworks, documentation standards, and quality controls for model development, validation, and lifecycle management
• Partner with Ops management to drive adoption of AI tools, define new processes, and train supply chain teams on insights-driven workflows
• Champion Samsara's cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally

Minimum requirements for the role:

• Experience & Education:

  • 8+ years in applied data science or ML, ideally in supply chain, operations research, logistics, or manufacturing
  • Master's or PhD in Computer Science, Statistics, Data Science, EE, OR, or related technical field

• Core ML & Technical Skills:

  • Expert in statistical modeling and ML: time series forecasting, optimization, anomaly detection, causal inference
  • Strong Python coding skills and fluency in SQL; proven experience developing and deploying production ML systems
  • Proficiency in MLOps practices: automated testing, CI/CD, model versioning, monitoring, performance tracking
  • Experience building real-time inference pipelines and managing GPU/TPU resources for training at scale
  • Familiar with data visualization tools (Tableau, Power BI) and cloud platforms (AWS, GCP, Azure)

• Mindset & Collaboration:

  • Passion for operational excellence, cost efficiency, and scalable solutions
  • Builder mindset, with a track record of taking products or systems from 0 to 1.
  • Exceptional problem-solving, critical thinking, and communication skills—able to explain complex technical concepts to non-technical stakeholders
  • Track record of collaborating cross-functionally and driving adoption of data-driven solutions

An ideal candidate also has:

• Deep Statistical Expertise: Able to design and validate experiments (A/B, multi-armed bandits) and apply Bayesian methods for uncertainty quantification.
• Time-Series Mastery: Proven track record building advanced forecasting models (e.g., Prophet, LSTMs, Transformer-based) for intermittent demand and seasonal patterns.
• Cost Optimization Mindset: Skilled in profiling ML workloads and driving down cloud/GPU spend through model compression (quantization, pruning) and serverless architectures.
• Cross-Region Scaling: Demonstrated ability to deploy low-latency inference across multiple geographic regions with fail-over and disaster-recovery strategies.
• Vendor & Open-Source Savvy: Experience evaluating and integrating third-party ML platforms and contributing to or leveraging relevant open-source projects.
• Domain Expertise: Deep understanding of supply chain concepts (S&OP, IBP, safety stock, EOQ), ERP systems (SAP, NetSuite, E2DP, Propel), and inventory optimization theory. Ideal background in consumer electronics or B2B hardware manufacturing.