Job Description
This role is categorized as hybrid. This means the successful candidate is expected to report to Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days].
The Role
We are looking for a hands-on Machine Learning Engineer with at least seven years of experience to productionize machine learning for manufacturing and to build the data engineering that those models depend on. This person will design, train, validate, and deploy models, and will design and operate the ETL/ELT pipelines, feature data, and governed Bronze, Silver, and Gold products those models run on. Current, working knowledge of Databricks is required.
What You’ll Do
Productionize manufacturing machine learning, including training, validation, batch scoring, deployment, monitoring, and retraining workflows in Databricks.
Design and build the data engineering underneath those models: ingestion, transformation, curation, feature pipelines, and governed tables and volumes in Unity Catalog.
Build reusable Bronze, Silver, and Gold pipelines and medallion patterns that analytics and ML both consume.
Use MLflow and Unity Catalog for experiment tracking, model registration, versioning, lineage, and controlled promotion across environments.
Ingest from enterprise and plant sources with managed connectors, DLT, Auto Loader, and structured streaming where those fit.
Partner with data science, architecture, product, manufacturing, and platform teams using Python, Spark, SQL, and Databricks-native tools.
Build CI/CD for model and data products, including tests, validation gates, deployment, code review, and operational handoff.
Improve reliability through data validation, evaluation sets, monitoring, alerting, documentation, and runbooks.
Support migration from legacy and on-prem ETL, and from proof-of-concept models, into production Databricks patterns.
Participate in operational support and on-call rotation for production data and ML pipelines.
Your Skills & Abilities (Required Qualifications)
6+ of experience in machine learning engineering, data engineering, or both.
Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent experience.
Proven experience deploying machine learning models into production and building the data or feature pipelines that support them.
Strong proficiency in Scala, Python, SQL, Spark, and Databricks, with production-quality software engineering practices.
Strong experience building and supporting cloud-based ETL/ELT pipelines, data platforms, and pipeline orchestration patterns for both batch and streaming operations.
Experience with MLflow or an equivalent model lifecycle: experiment tracking, registry, versioning, and promotion across environments.
Strong experience with Databricks jobs, workflows, repos, Unity Catalog, and MLflow-based experimentation and model lifecycle management.
CI/CD and GitHub-based deployment of data and ML products.
Experience delivering production pipelines with data science, architecture, product, and business partners.
Strong troubleshooting, communication, and technical leadership.
What Can Give You a Competitive Advantage (Preferred Qualifications)
Data science experience is a plus, including building and training a model, feature design, model evaluation, and working with data scientists to turn experiments into production candidates.
Experience deploying a model into an isolated network, such as a plant or other restricted environment, where the runtime is separated from the training platform.
Scala, Azure Event Hub, Kafka, or Pulsar.
DLT, Auto Loader, structured streaming, and metadata-driven pipeline frameworks.
Model monitoring, drift detection, validation gates, and production-readiness reviews.
Manufacturing data, plant-floor systems, industrial telemetry, or manufacturing quality use cases.
Legacy ETL modernization or the move from proof of concept into a governed production…