Sr. AI/Machine Learning Engineer - Supply Chain
US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.Full-TimeSenior
Salary170170 - 257400 USD per year
Apply NowOpens the employer's application page
Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonSQLGCPMachine LearningMicrosoft Power BITableauAzureMLOps
Requirements
- 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.
- Expert in statistical 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).
- Familiar with cloud platforms (AWS, GCP, Azure).
- Passion for operational excellence, cost efficiency, and scalable solutions.
- Exceptional problem-solving, critical thinking, and communication skills.
- Track record of collaborating cross-functionally and driving adoption of data-driven solutions.
Responsibilities
- 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.
View Full Description & ApplyYou'll be redirected to the employer's site
