Machine Learning Engineer

Dow•Midland, MI

8d•Hybrid

About The Position

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 DevOps engineers, application teams, domain experts, cybersecurity partners, and business stakeholders.

Requirements

A minimum of a bachelor’s degree or relevant military experience at the E5 rank/Petty Officer 2nd Class or higher or 8 years of experience in lieu of a bachelor’s degree is required.
A minimum of three years of experience developing and delivering solutions in machine learning, data science, software engineering, data engineering, or a related field.
The minimum requirement for this U.S.-based position is the ability to work legally in the United States. No visa sponsorship or support is available for this position, including for any type of U.S. permanent residency process.

Nice To Haves

Degree in computer science, engineering, mathematics, statistics, data science, or a related field. Additional preference for an advanced degree from a relevant field.
Demonstrated experience architecting or operating an enterprise MLOps framework, platform, or shared set of deployment practices.
Advanced proficiency in Python, PySpark, and SQL, with experience using one or more machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, Keras, Spark MLlib, or Ray.
Hands-on experience developing and deploying machine learning models and pipelines on Databricks, including MLflow, Delta Lake, Unity Catalog, Workflows or Jobs, model registry capabilities, and batch or real-time serving.
Experience implementing modern software engineering practices for ML workloads using Git, automated testing, code review, CI/CD, packaging, infrastructure as code, and Azure DevOps.
Strong knowledge of machine learning concepts, algorithms, evaluation methods, feature engineering, model selection, optimization, explainability, and production monitoring.
Experience designing and operating batch, streaming, and real-time inference patterns, including model integration through REST APIs, event platforms such as Event Hubs or Kafka, or enterprise batch interfaces.
Experience with Azure services such as Azure Machine Learning, Azure Data Factory, Azure Data Lake Storage Gen2, Functions, Logic Apps, Azure SQL, or Azure Kubernetes Service.
Experience designing and deploying both traditional machine learning and generative AI systems into production.
Knowledge of data modeling, lakehouse architecture, data warehousing, ETL/ELT, Apache Spark, and distributed data processing.
Demonstrated ability to influence technical communities, define standards, facilitate architecture decisions, coach practitioners, and communicate complex concepts to technical and non-technical audiences.
Ability to lead complex, cross-functional work, manage priorities across multiple projects with limited supervision, and resolve ambiguous technical and organizational challenges.
Curiosity and a demonstrated ability to evaluate and apply emerging technologies to business objectives.
Excellent consulting skills and the ability to communicate effectively across diverse technical and business audiences, translating complex concepts into clear, actionable recommendations.

Responsibilities

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 DevOps.
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 development leaders to align practices, remove delivery friction, and establish clear ownership across the ML lifecycle.
Define and monitor meaningful measures of framework adoption, solution quality, deployment speed, reliability, maintainability, operational health, and business value; use the results to guide continuous improvement.
Serve as a trusted technical advisor and mentor, helping teams make pragmatic architecture and engineering decisions for traditional machine learning and generative AI solutions.
Operate as a hands-on ML engineer on high-priority use cases, translating business, analytical, security, integration, scalability, and service-level requirements into production solution designs.
Design and implement training, validation, batch inference, streaming inference, and real-time serving pipelines on Azure and Databricks.
Use MLflow and related Databricks capabilities for experiment tracking, evaluation, model registration, deployment, observability, and lifecycle management.
Perform or support data analysis, feature engineering, model selection, hyperparameter optimization, model evaluation, packaging, and production validation as needed across the end-to-end lifecycle.
Collaborate with application development teams to integrate model outputs or serving endpoints securely and reliably into production IT systems using APIs, events, streams, or batch interfaces.
Implement monitoring and support practices for data quality, system performance, prediction quality, drift, failures, and cost; lead troubleshooting and remediation for production ML solutions.
Communicate solution designs, tradeoffs, model performance, operational risks, and business outcomes clearly to technical and non-technical stakeholders.

Benefits

Equitable and market-competitive base pay and bonus opportunity across our global markets, along with locally relevant incentives.
Benefits and programs to support your physical, mental, financial, and social well-being, to help you get the care you need...when you need it.
Competitive retirement program that may include company-provided benefits, savings opportunities, financial planning, and educational resources to help you achieve your long term financial-goals.
Employee stock purchase programs (availability varies depending on location).
Student Debt Retirement Savings Match Program (U.S. only).
Robust medical and life insurance packages that offer a variety of coverage options to meet your individual needs. Travel insurance is also available in certain countries/locations.
Opportunities to learn and grow through training and mentoring, work experiences, community involvement and team building.
Workplace culture empowering role-based flexibility to maximize personal productivity and balance personal 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’s (ERG) participation.
Wellbeing Portal for all Dow employees, our one-stop shop to promote wellbeing, empowering employees to take ownership of their entire wellbeing journey.
On-site fitness facilities to help stay healthy and active (availability varies depending on location).
Employee discounts for online shopping, cinema tickets, gym memberships and more.
Transportation allowance (availability varies depending on location)
Meal subsidiaries/vouchers (availability varies depending on location)
Carbon-neutral transportation incentives e.g. bike to work (availability varies depending on location)