Engineer, Machine Learning

Venture Global LNG•Arlington, VA

2d•$157,000 - $185,000•Onsite

About The Position

The Machine Learning Engineer will design, develop, and maintain the productionization of machine learning, deep learning, generative AI, large language models, simulation, and optimization algorithms. The role includes building training and deployment pipelines and enabling models to run efficiently in production. Data engineering work will primarily be done in Databricks and PySpark. The position is based in Arlington, Virginia, and requires commuting to the office five days a week. It reports to the Director of Business Intelligence and is structured within IT under the Vice President of Applications. The ideal candidate has strong technical proficiency and communication skills, is self-driven, and is willing to continuously learn.

Requirements

At least 5 years of machine learning engineering, software engineering, or data science experience.
Bachelor’s degree in a quantitative field of study.
Excellent technical proficiency and communication skills.
Self-driven mindset and willingness to continuously learn.

Nice To Haves

Master’s degree in a quantitative field of study.
Experience with Azure, AWS, or other cloud ecosystems.
Experience building secure data-processing pipelines.
Proficiency with data lakes, CI/CD pipelines, Databricks, Unity Catalog, and Git.
Experience working with streaming.
Expertise building machine learning solutions using cloud data services.
Exceptional skills in data-processing languages such as SQL, Python, or Scala.
Exceptional skills in feature engineering, model optimization, and parameter tuning.

Responsibilities

Work with business stakeholders to define project requirements.
Orchestrate, scale, set up, and improve model-serving pipelines.
Improve model accuracy through feature engineering, tuning, and observability.
Improve model computational performance across the pipeline, including cluster and job-compute tuning, partitioning, caching, feature-engineering code, and tuning setup.
Integrate machine learning models into production environments, ensuring reliability and scalability.
Evaluate pretrained models and vendor software and support their integration into production environments.
Develop project plans for machine learning and AI implementations, including solution architectures, resourcing, and dependencies.
Provide ETL requirements to data engineers to curate files for data analytics.
Work with data scientists, data engineers, and business analysts to translate business requirements into machine learning solutions.
Build maintainable, scalable software solutions that provide quantifiable business value.
Focus on quality architecture, quality code, and management of technical debt.
Learn and test newer and better ways of performing work to advance the practice.