AI/ML Engineer, Supply Chain Analytics (Remote)

Samsara; Seattle (WA); Remote; USD 180,000 - 300,000; Full time

Benefits offered by this job

Flexible working

Remote-friendly

Professional dev stipend

Parental leave

Health insurance

Job summary

Samsara is seeking an experienced AI/ML Engineer to help build and deploy production-grade models for our supply chain operations. You will work across fulfillment, inventory, planning, and finance to forecast demand, optimize inventory, and assess supplier risk using real-time IoT signals.

Ideal candidates have 8+ years in data science, a Master’s or PhD, and a strong track record in MLOps, time-series forecasting, and scalable models.

Qualifications

• 8+ years in data science or ML with a focus on supply chain, operations, or manufacturing.
• Experience designing, validating, and deploying ML models in production.
• Strong communication and collaboration skills across cross-functional teams.

Responsibilities

• Define the end-to-end AI transformation roadmap for supply chain with leadership alignment.
• Design, train, validate, and deploy ML models to production (forecasting, inventory, supplier risk).
• Build predictive models for demand, spend, and lead times across the global supply chain.
• Create features from ERP, IoT, and third-party data; build pipelines and ETL for stakeholders.
• Deliver production-grade code supporting batch and real-time inference with MLOps best practices.
• Mentor junior scientists and act as a scientific voice in roadmap and experiments.

Skills

Python

SQL

Time-series forecasting

MLOps

Production ML

Cross-functional

Education

MS/PhD in Computer Science / Data Science

Tools

AWS

GCP

Tableau

Power BI

ERP systems

Job description

Samsara is seeking an experienced AI/ML Engineer to help build and deploy production-grade models for our supply chain operations. You will work across fulfillment, inventory, planning, and finance to forecast demand, optimize inventory, and assess supplier risk using real-time IoT signals.

Ideal candidates have 8+ years in data science, a Master’s or PhD, and a strong track record in MLOps, time-series forecasting, and scalable models.