Sr. AI/Machine Learning Engineer - Supply Chain

About the role:

We are seeking a Senior Applied Scientist II to join our Supply Chain Innovation & Intelligence team. In this role, you’ll be responsible for designing, developing, and deploying advanced machine learning and statistical models that optimize and transform our global supply chain. From forecasting demand across thousands of SKUs to modeling supply risk using real-time IoT signals, you’ll apply cutting-edge techniques to solve complex problems with direct business impact.

You’ll work alongside data scientists, software engineers, and domain experts, collaborating cross-functionally with teams in Product, Engineering, Procurement, and Finance. You’ll report to the Director of Supply Chain Innovation & Intelligence and play a key role in shaping the team’s roadmap, scientific agenda, and modeling standards.

This role is ideal for a deeply technical IC who thrives in ambiguity, thinks strategically, and enjoys end-to-end ownership—from ideation to deployment.

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.

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 Director, aligning with company OKRs and working closely with executive stakeholders.
• Own the design, training, validation, and deployment of ML and statistical models.
  (e.g., demand forecasting, inventory optimization, supplier risk scoring), 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, role model, and embed 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 and across new offices.

Minimum requirements for the role:

• 8+ years of experience in applied data science or machine learning, ideally in supply chain, operations research, logistics, or manufacturing.
• Master’s or PhD in Computer Science, Statistics, Data Science, EE, OR, or a related technical field.
• Expertise in statistical modeling or machine learning, including time series forecasting, optimization, and anomaly detection.
• Strong coding skills in Python and fluency in SQL; experience developing and deploying production ML systems.
• Familiarity with MLOps practices, including automated testing, CI/CD, model versioning, and monitoring.
• Demonstrated track record building real-time inference pipelines and managing GPU/TPU resources.
• Familiarity with data visualization tools (e.g., Tableau, Power BI) and cloud platforms (e.g., AWS, GCP, or Azure).
• A passion for operational excellence, cost efficiency, and building scalable, data-driven solutions.
• Exceptional problem-solving, critical thinking, and communication abilities.

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.