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 & Analytics organization located in Houston, TX;
Midland, MI; or Champaign, IL.

As a Machine Learning Engineer in our Data & Analytics Platforms team specializing in data and analytics solutions, you will work with a cross-functional team whose objective is to deliver solutions that drive business value for Dow. In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and inference pipelines on the Azure Databricks platform.

You will also help drive a culture around setting standards and adopting best practices for machine learning and MLOps across the organization.

• Designs and implements pipelines and other workflow infrastructure to meet the requirements for new AI/ML solutions that involve online, batch, or real-time inference

• Deploys and monitors machine learning models in production using Databricks Model Registry, Jobs, and Workspace

• Frequently collaborates with data engineers, Dev Ops/platform engineers, data scientists, and domain experts as part of a comprehensive MLOps framework to ensure AI/ML solutions are performant, reliable, and maintainable

• Works frequently with application development teams to ensure seamless integrations

• Works proficiently with various ML frameworks, such as scikit-learn, Tensor Flow, PyTorch, Keras, as well as distributed frameworks like Spark MLlib and Ray

• Performs data analysis, feature engineering, model selection, hyperparameter optimization, and model evaluation using Databricks MLFlow, Delta Lake, and SQL Analytics and other tools as part of the end-to-end ML lifecycle

• Researches and implements new machine learning techniques and methods using Databricks, staying abreast of the latest trends and technologies

• Documents and communicates machine learning results and insights to stakeholders using Databricks notebooks and dashboards

• Understands IT security policies and implements them as part of new solution designs

• Follows and promotes the best practices and standards for machine learning and MLOps across the organization using Databricks and Azure Dev Ops

• Solutions Delivery:
  End-to‑end ownership of Azure data solutions—translating ambiguous business needs into robust architectural blueprints, then delivering through design, build, test, deployment, and post‑launch monitoring with a focus on reliability, performance, and cost efficiency.

• Cloud Computing:
  Designing and operating cloud‑native architectures on Azure (e.g., Databricks Lakehouse, ADF/Workflows, Functions, Logic Apps, Azure SQL) with CI/CD and IaC to scale securely and economically for ML, BI, streaming, and web applications.

• Integration Services:
  Building secure, high‑throughput integrations—REST APIs and event/stream pipelines (e.g., Event Hubs/Kafka)—to connect polyglot data stores (SQL Server, Cosmos DB, Neo4j) and enable real‑time and batch data products.

• Security Awareness:
  Embedding governance, identity, and compliance into the architecture (OAuth/RBAC, data governance, re‑authorization/ownership verification), enforcing code reviews and automated controls across pipelines and deployments.

• Strategic Planning:
  Aligning platform roadmaps and technology choices with enterprise strategy, mentoring engineers on best practices, defining standards and KPIs, and prioritizing…