Remote Staff ML Engineer, AI for Supply Chain & Forecasting

Hidden Jobs; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Remote-first arrangement US/Canada

Professional development stipend

Comprehensive health coverage

Parental leave

Job summary

Hidden Jobs is seeking a staff-level machine learning engineer to build 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.

Qualifications

• Must have experience designing and deploying ML models in production with end-to-end ownership.
• Strong engineering skills to build scalable data pipelines and serving infra.
• Practical knowledge of MLOps for batch and real-time inference.

Responsibilities

• Define the AI transformation roadmap for supply chain operations aligned with OKRs.
• Design, train, validate, and deploy production ML models for forecasting and optimization with ROI.
• Build predictive models using ERP, IoT, and third-party data for batch and real-time inference.
• Deliver production-grade code and robust MLOps, monitoring, and infra improvements.
• Partner with Product, Engineering, Procurement, and Finance to align data and change management.
• Mentor junior scientists through code reviews and roadmap discussions.

Skills

ML production

Data pipelines

MLOps

Cross-functional collaboration

Mentoring

Job description

Hidden Jobs is seeking a staff-level machine learning engineer to build 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.