Sr. AI/Machine Learning Engineer - Supply Chain

About the role:

This is a founding AI/ML role in Samsara's Supply Chain organization-a rare 0-to-1 opportunity to build AI/ML capabilities from the ground up and own demand forecasting, inventory optimization, and supplier risk modeling end-to-end.

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, E2DP, 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: Report to our SVP of Operations, collaborating cross-functionally with teams in Product, Engineering, Procurement, and Finance

You'll shape the team's roadmap, scientific agenda, and modeling standards. Ideal for a deeply technical IC who thrives in ambiguity, thinks strategically, and enjoys end-to-end ownership.

This role is open to candidates residing in the US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.

Why this role matters:

Real-world impact at massive scale:

• Your forecasting models will help prevent supply shortages for customers in construction, utilities, transportation, and public safety-industries that literally keep the world running
• Optimize inventory for hardware deployed across 98% of US roads and 70+ billion miles driven annually
• Drive sustainability initiatives by reducing waste and improving supply chain efficiency across global operations

Unique technical challenges:

• Build demand 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-not just historical sales
• 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

A data playground unlike any other:

• Access to 10+ trillion data points annually from connected operations
• Work with ERP data, IoT sensor streams, third-party market intelligence, and operational metrics
• Build features from unique real-world signals-vehicle diagnostics, utilization patterns, failure modes

You should apply if:

• You are motivated by impact: Your work will drive smarter, faster, and more sustainable supply chain decisions across Samsara's operations.
• You combine scientific rigor with real-world pragmatism: You're skilled at navigating trade-offs between model complexity, performance, and maintainability.
• You want to work on hard problems: You'll help tackle forecasting volatility, cost optimization, anomaly detection, and more using large, noisy, high-dimensional datasets.
• You are a self-directed leader: You identify analytical white space, set multi-quarter plans, and mentor others through influence.
• You enjoy building models that matter: Your solutions will be deployed in production, drive decisions, and be regularly evaluated against business KPIs.

In this role, you will:

• Define the end-to-end AI transformation roadmap for supply chain alongside the SVP of Operations, 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), ensuring robust MLOps practices and measurable ROI.
• Build predictive models to forecast demand, lead times, and cellular 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.
• Hire, develop and lead an inclusive, engaged, and high-performing team.
• 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 instatistical 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:

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  • Passion for operational excellence, cost efficiency, and scalable solutions.
  • 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.