Machine Learning Engineer

Remote with a preference on local. And if a local candidate is chosen, there may be an onsite requirement.

Job Summary

This role supports the development and modernization of the demand forecasting capabilities within the client's digital fulfillment organization. The team is responsible for forecasting order volumes, units, and fulfillment capacity across multiple channels (OPU, Ship-to-Home, Drive Up) to optimize store operations planning.

Working closely with data scientists and platform engineers, this role bridges ML research and production by scaling data processing workloads, building robust ML pipelines, and ensuring forecasting models run reliably at scale.

The ideal candidate brings an ML engineering mindset—combining data engineering, pipeline orchestration, and software engineering skills—to modernize a complex forecasting ecosystem that directly impacts store labor planning and customer experience.

Technical Skills: Must Have

Machine Learning & Data Science

Experience building and deploying ML models in production environments

Hands-on experience with time series forecasting (Prophet, ARIMA, or similar)

Understanding of hyperparameter tuning, model validation, and experiment tracking

Familiarity with feature engineering and feature store concepts

Data Engineering & Scalability

Proficiency converting pandas-based workloads to PySpark for large-scale processing

Experience with distributed data processing frameworks (Spark, Dask, or Ray)

Ability to optimize data pipelines for performance and cost efficiency

Working knowledge of data formats (Parquet, CSV) and partitioning strategies

Experience with BigQuery or similar analytical databases (table design, partitioning, clustering, writing/validating datasets)

ML Pipeline Orchestration

Experience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or Airflow

Understanding of pipeline compo