Staff MLE - Supply Chain

Role overview A staff-level machine learning engineer position focused on building AI-native capabilities for a global hardware supply chain. The role sits within an internal operations organization and covers demand forecasting, inventory optimization, supplier risk scoring, and cost modeling across fulfillment, planning, procurement, finance, and engineering functions. It is a remote opportunity open to candidates in the US or Canada.

Responsibilities - Define the end-to-end AI transformation roadmap for supply chain operations, aligning with company OKRs and executive stakeholders. - Design, train, validate, and deploy production machine learning models covering demand forecasting, inventory optimization, supplier risk, cellular spend prediction, and hardware cash flow, with measurable ROI. - Build predictive models and feature pipelines that combine ERP records, large-scale IoT telemetry, and third-party datasets for both batch and real-time inference. - Deliver production-grade code and robust MLOps practices, including monitoring and infrastructure enhancements for real-time training and inference at scale. - Act as an AI liaison to Product, Engineering, Procurement, and Finance teams, ensuring data, integration, and change-management alignment. - Mentor junior scientists through code reviews, collaborative projects, and as a key scientific voice in roadmap and modeling-strategy discussions.

Requirements - Significant experience designing and deploying machine learning and statistical models in production environments with end-to-end ownership. - Strong engineering skills for building scalable data pipelines, ETL jobs, and serving infrastructure over large, multimodal datasets. - Practical knowledge of MLOps practices for both batch and real-time inference, including monitoring, validation, and reliability engineering. - Ability to work across highly variable, seasonal product portfolios with intermittent demand patterns and long hardware lead times. - Comfort partnering with cross-functional stakeholders in planning, procurement, fulfillment, finance, and hardware or quality engineering. - A track record of operating effectively in ambiguous environments and shaping scientific direction and modeling standards.

Nice to have - Background applying machine learning to physical operations or hardware supply chains, including multi-tier supplier networks. - Experience modeling supplier risk using real-time telemetry, geopolitical signals, or market data alongside historical sales. - Familiarity with anomaly detection for spend or supply-chain disruption using multimodal data sources.

Benefits and work setup - Remote-first arrangement open to candidates in the US or Canada, with in-person office collaboration supported when it adds value. - Compensation package that includes a professional development stipend along with comprehensive health coverage and parental leave plans. - Equal opportunity employer committed to inclusive hiring and accessible interview processes.