ML (Data) Platform Engineer

TL;DR

ML (Data) Platform Engineer (AutoML): Building scalable data infrastructure and pipelines powering AutoML platforms for ML and analytics use cases with an accent on feature engineering, time series forecasting, and data management systems. Focus on owning feature stores, implementing data SLAs and lineage, and driving composable architectures for supply chain decision intelligence.

Company

Enterprise AI platform for supply chains offering out-of-the-box AI solutions for optimization, forecasting, and scenario analysis, plus composable tools for business and technical users. Used by Fortune 500 companies including Coca-Cola and Estée Lauder.

What you will do

• Build and scale data management systems powering AutoML and forecasting platforms
• Own and evolve feature store and feature engineering workflows
• Implement robust data SLAs and lineage systems across time series pipelines
• Collaborate with ML engineers, infra, and product teams for scalable platform design
• Drive architectural decisions on data distribution, versioning, and composability
• Participate in designing reusable systems for supply chain problems

Requirements

• Strong experience with large-scale data systems (Big Data, distributed pipelines)
• Hands-on experience with ETL pipelines, data lineage, and data reliability tooling
• Proven experience in ML feature engineering and/or building feature stores
• Exposure to time series data, forecasting pipelines, or AutoML workflows
• Strong problem-solving and design thinking for ambiguous platform problems

Nice to have

• Familiarity with modern data infra (Apache Iceberg, ClickHouse, Data Lakes)
• Product thinking to anticipate user interactions
• Experience building composable, user-extensible systems
• Prior exposure to AutoML frameworks (SageMaker, VertexAI) or internal ML platforms