Burtch Works
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Machine Learning Engineer
role at
Burtch Works
in Seattle, WA (onsite 4 days a week).
About The Company Burtch Works is a leading entertainment company committed to building world‑class digital experiences across all platforms. Our organization is dedicated to innovation, collaboration, and delivering exceptional user experiences that connect audiences with the content they love. We foster a culture of curiosity, excellence, and strategic thinking where talented professionals can thrive and make a meaningful impact.
Job Summary We are looking for a Machine Learning Engineer to join our team in Seattle. The ideal candidate will be a collaborative technical professional with strong expertise in data engineering and machine learning model development. This role will involve partnering with data scientists and engineers to build and deploy large‑scale feature sets and machine learning models in production environments.
Key Responsibilities
Feature Engineering and Optimization: Acquire new data and conform it to usable model features. Develop and maintain pipelines using orchestration and data management tools. Develop and deploy scalable streaming and batch data pipelines to support petabyte‑scale datasets for feature development.
Model Development, Deployment, and Optimization: Work together with data scientists and engineers to build and deploy machine learning models in large‑scale production environments. Ensure models are optimized for performance and scalability.
Best Practices and Standards: Maintain existing and establish new development, testing, and deployment standards. Ensure code quality, documentation, and adherence to engineering best practices across the ML lifecycle.
Collaboration and Innovation: Identify and define opportunities for data collection, feature development, model development, testing, monitoring, and experimentation. Partner cross‑functionally to drive continuous improvement.
Data Governance: Work with data subject to privacy, governance, and legal policies, ensuring compliance and security throughout all data operations.
Requirements
Education: Bachelor's degree in Computer Science, Mathematics, or related fields.
Experience: 5+ years of relevant experience in data engineering and machine learning.
Skills:
Expertise in Python and SQL.
Proficiency with Spark, Snowflake, and dbt.
Strong experience with data engineering technology and best practices.
Experience building, training, and deploying machine learning models.
Experience working in AWS environments.
Other: Experience working with data subject to privacy, governance, and legal policies.
Preferred Qualifications
Proficiency in Databricks and Snowflake in AWS environments.
Experience working with machine learning frameworks such as SparkML, scikit-learn, PyTorch, and Keras.
Experience with Github, Airflow, Scala, and Java.
Proficiency in DevOps and CI/CD practices.
Experience with multiple model deployment tools.
Experience with MLflow or other ML tracking tools.
Experience working with Agile teams.
Experience with data science focused feature stores.
Strong data analysis and visualization skills.
Experience applying mathematical and statistical methods to data.
Seniority Level Mid‑Senior level
Employment Type Temporary
Job Function Engineering and Information Technology
Industries IT Services and IT Consulting
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Machine Learning Engineer
role at
Burtch Works
in Seattle, WA (onsite 4 days a week).
About The Company Burtch Works is a leading entertainment company committed to building world‑class digital experiences across all platforms. Our organization is dedicated to innovation, collaboration, and delivering exceptional user experiences that connect audiences with the content they love. We foster a culture of curiosity, excellence, and strategic thinking where talented professionals can thrive and make a meaningful impact.
Job Summary We are looking for a Machine Learning Engineer to join our team in Seattle. The ideal candidate will be a collaborative technical professional with strong expertise in data engineering and machine learning model development. This role will involve partnering with data scientists and engineers to build and deploy large‑scale feature sets and machine learning models in production environments.
Key Responsibilities
Feature Engineering and Optimization: Acquire new data and conform it to usable model features. Develop and maintain pipelines using orchestration and data management tools. Develop and deploy scalable streaming and batch data pipelines to support petabyte‑scale datasets for feature development.
Model Development, Deployment, and Optimization: Work together with data scientists and engineers to build and deploy machine learning models in large‑scale production environments. Ensure models are optimized for performance and scalability.
Best Practices and Standards: Maintain existing and establish new development, testing, and deployment standards. Ensure code quality, documentation, and adherence to engineering best practices across the ML lifecycle.
Collaboration and Innovation: Identify and define opportunities for data collection, feature development, model development, testing, monitoring, and experimentation. Partner cross‑functionally to drive continuous improvement.
Data Governance: Work with data subject to privacy, governance, and legal policies, ensuring compliance and security throughout all data operations.
Requirements
Education: Bachelor's degree in Computer Science, Mathematics, or related fields.
Experience: 5+ years of relevant experience in data engineering and machine learning.
Skills:
Expertise in Python and SQL.
Proficiency with Spark, Snowflake, and dbt.
Strong experience with data engineering technology and best practices.
Experience building, training, and deploying machine learning models.
Experience working in AWS environments.
Other: Experience working with data subject to privacy, governance, and legal policies.
Preferred Qualifications
Proficiency in Databricks and Snowflake in AWS environments.
Experience working with machine learning frameworks such as SparkML, scikit-learn, PyTorch, and Keras.
Experience with Github, Airflow, Scala, and Java.
Proficiency in DevOps and CI/CD practices.
Experience with multiple model deployment tools.
Experience with MLflow or other ML tracking tools.
Experience working with Agile teams.
Experience with data science focused feature stores.
Strong data analysis and visualization skills.
Experience applying mathematical and statistical methods to data.
Seniority Level Mid‑Senior level
Employment Type Temporary
Job Function Engineering and Information Technology
Industries IT Services and IT Consulting
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