Machine Learning Engineer

Dow’s Enterprise Data & Analytics organization is seeking a Machine Learning Engineer to design, develop, and deploy machine learning systems across online, batch, and real-time use cases. The role is onsite in Houston, TX; Midland, MI; or Champaign, IL and focuses on building and maintaining production-ready solutions using Azure Databricks and associated MLOps tooling.

Role Overview

In this position, you will engineer end-to-end machine learning workflows, deploy models to production, and support reliable operations through monitoring and strong MLOps practices. You will collaborate with cross-functional teams, including data engineering, DevOps/platform engineering, data science, and application development, to deliver performant and maintainable AI/ML capabilities. Work includes documenting results and insights for stakeholders using Databricks notebooks and dashboards, while aligning designs with IT security policies.

Key Responsibilities

• Design and implement pipelines and workflow infrastructure for new AI/ML solutions supporting online, batch, and real-time inference.
• Deploy and monitor machine learning models in production using Databricks Model Registry, Jobs, and Workspace.
• Collaborate within a comprehensive MLOps framework with data engineers, DevOps/platform engineers, data scientists, and domain experts to support performance, reliability, and maintainability.
• Work closely with application development teams to support seamless integration.
• Use and apply machine learning frameworks including scikit-learn, TensorFlow, PyTorch, Keras, and distributed frameworks such as Spark MLlib and Ray.
• Perform data analysis and feature engineering; support model selection, hyperparameter optimization, and evaluation across the end-to-end ML lifecycle using Databricks MLflow, Delta Lake, SQL Analytics, and other tools.
• Research and implement new machine learning techniques and methods using Databricks while staying current on trends and technologies.
• Document and communicate machine learning results and insights to stakeholders using Databricks notebooks and dashboards.
• Understand IT security policies and incorporate them into new solution designs.
• Follow and promote organizational machine learning and MLOps best practices and standards using Databricks and Azure DevOps.

Required Qualifications

• A minimum of a Bachelor’s degree, or 8 years of relevant experience, or relevant military experience at an E6 rank / Petty Officer 2nd Class or higher.
• At least 3 years of experience developing solutions in machine learning, data science, or a related field.
• Ability to work legally in the United States. No visa sponsorship/support is available for this position, including for any U.S. permanent residency (green card) process.

Technologies

Azure Databricks, Databricks Model Registry, Databricks Jobs, Databricks Workspace, scikit-learn, TensorFlow, PyTorch, Keras, Spark MLlib, Ray, Databricks MLflow, Delta Lake, SQL Analytics, Databricks notebooks, Databricks dashboards, Azure DevOps, Azure Data Factory, Azure Workflows, Functions, Logic Apps, Azure SQL, CI/CD, IaC, Event Hubs, Kafka, SQL Server, Cosmos DB, Neo4j, OAuth, RBAC, Apache Spark, Hive, Azure Machine Learning, Azure Kubernetes Service, Azure Data Lake Storage Gen2, SQL, REST APIs.

Benefits

• Equitable and market-competitive base pay and bonus opportunity across global markets, with locally relevant incentives.
• Benefits and programs supporting physical, mental, financial, and social well-being.
• Competitive retirement program that may include company-provided benefits, savings opportunities, financial planning, and educational resources.
• Employee stock purchase programs (availability varies by location).
• Student Debt Retirement Savings Match Program (U.S. only).
• Robust medical and life insurance packages with a variety of coverage options.
• Training and mentoring opportunities through learning experiences, team building, community involvement, and growth support.
• Workplace culture supporting role-based flexibility to maximize personal productivity and balance needs.
• Competitive yearly vacation allowance.
• Paid time off for new parents (birthing and non-birthing, including adoptive and foster parents).
• Paid time off to care for family members who are sick or injured.
• Paid time off to support volunteering and Employee Resource Group (ERG) participation.
• Wellbeing Portal for Dow employees.
• On-site fitness facilities (availability varies by location).
• Employee discounts for online shopping, cinema tickets, gym memberships, and more.
• Transportation allowance (availability varies by location).
• Meal subsidies/vouchers (availability varies by location).
• Carbon-neutral transportation incentives such as bike to work (availability varies by location).

Preferred Qualifications

• Degree in computer science, engineering, mathematics, statistics, data science, or a related field.
• Proficiency in Python and one or more machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience developing and deploying machine learning models and pipelines on Databricks using Databricks MLflow, Delta Lake, SQL Analytics, Model Registry, Jobs, and Workspace.
• Strong knowledge of machine learning concepts, techniques, and algorithms.
• Ability to perform data analysis, feature engineering, model selection, optimization, and evaluation using Databricks.
• Ability to communicate complex machine learning concepts and results to technical and non-technical audiences using Databricks notebooks and dashboards.
• Ability to work independently and collaboratively in a fast-paced, dynamic environment.
• Curiosity and passion for learning new machine learning skills and technologies using Databricks.
• Strong knowledge of data modeling, data warehousing, and ETL processes.
• Experience designing and deploying into production both traditional and generative AI systems.
• Proficiency in SQL and experience with big data technologies such as Apache Spark and Hive.
• Experience working within Azure Machine Learning.
• Experience containerizing and deploying ML models to Azure Kubernetes Service.
• Experience with Azure Data Factory, Azure Data Lake Storage Gen2, and other Azure services.
• Multi-application and cross-platform design experience.
• Understanding of data lakehouse platform design and associated workflows.
• Ability to thrive in challenging situations and solve complex problems.
• Ability to manage own work effort across multiple projects with little supervision.
• Interest in emerging technologies with the ability to quickly learn and apply cutting-edge offerings to achieve business objectives.

Additional Notes

• No relocation assistance is offered for this position.
• This position does not have people leadership responsibility. It is an Independent Contributor role; however, you may coach and mentor junior resources.