Machine Learning Engineer

At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet.

Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple - to deliver a sustainable future for the world through science and collaboration.

If you’re looking for a challenge and meaningful role, you’re in the right place.

Dow has an exciting and challenging opportunity for a Machine Learning Engineer within our Enterprise Data & AI organization, located in Houston, TX;
Midland, MI; or Champaign, IL.

As a Machine Learning Engineer on our Data Science & Engineering team, you will serve in a multi-faceted technical leadership role spanning three key areas: MLOps Framework Architecture, Engineering Excellence, and Use Case Delivery. You will architect, manage, support, and continuously enhance the tools, technology, and processes that enable data science teams to move reliably through the end-to-end machine learning lifecycle using a Databricks-first approach.

You will also lead the adoption of modern software engineering and MLOps practices across Dow’s data science community and work hands‑on as an ML engineer on priority projects, designing and implementing production‑grade model deployments and integrations with enterprise IT systems. Success in this role requires close collaboration with data scientists, data engineers, platform and Dev Ops engineers, application teams, domain experts, cybersecurity partners, and business stakeholders.

• Own the architecture and technical roadmap for Dow’s enterprise MLOps framework, integrating the tools, technology, environments, controls, and processes required to move machine learning assets from development through production successfully with a Databricks-first approach.
• Design repeatable workflows for development, testing, release, deployment, monitoring, retraining, and retirement across appropriately separated development, test, and production environments that incorporate best practices and meet security requirements.
• Build and enhance CI/CD, configuration-as-code, and infrastructure-as-code patterns for ML code, data pipelines, models, and supporting services using Databricks and Azure Dev Ops.
• Establish reusable reference architectures, templates, libraries, automated tests, quality gates, deployment patterns, observability, and operational support practices for batch, streaming, and real‑time inference.
• Implement secure and governed lifecycle management for data, features, experiments, models, and deployments, including access control, lineage, auditability, versioning, and model discovery.
• Evaluate and incorporate emerging Databricks, Azure, open-source, and generative AI capabilities when they improve reliability, developer productivity, governance, scalability, or cost efficiency.

• Define, document, and promote modern development standards for machine learning solutions, including source control, branching, code reviews, modular design, automated testing, dependency management, reproducibility, CI/CD, and production readiness.
• Drive adoption of Dow’s MLOps framework across the company’s data science community through coaching, hands‑on enablement, reusable examples, technical reviews, office hours, and targeted learning.
• Partner with data science, data engineering, platform, architecture, cybersecurity, and application…