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Beaverton, OR, US

The role of an ML Engineer is an intersection of Data Scientist, Data Engineer, and DevOps Engineer. You'll be working in a team of ML engineers that takes on a wide array of responsibilities that encompass building of all the infrastructure necessary to take a trained ML Model and making it consumable by the client. They're also a line of defense against threats to the health of models (such as emerging issues with data and poor performance against new trends), as well as a team that provides support to Data Scientists working on models in the development phase. This wide skillset required for ML Engineering is why the perfect candidate is a unicorn, and thus we aim to assemble teams of complimenting and well-rounded individuals to fulfill such a role.


  • Work with teams to design and build cloud hosted, automated pipelines that run, monitor, and retrain ML Models for business applications
  • Design and implement Model and Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, and other ML Engineers
  • Optimize and refactor development code so that it can be moved to production
  • Build ETL Pipelines for new and existing models
  • Requisition cloud infrastructure for model and pipeline development environments
  • Assemble configurations and specifications to automatically build environments in production
  • Create and develop in CI/CD Pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases
  • Demo new projects and features to stakeholders and excited team members
  • An enthusiasm to ask questions of team members and learn new things is essential, as nobody can be expected to know everything. We pride ourselves in being one of the most supportive teams in the business, and we all build off one another to achieve great things


  • University or advanced degree in engineering, computer science, mathematics, or a related field
  • 5+ years experience developing and deploying machine learning systems into production
  • Hands on experience containerizing code and environments with Docker and/or Kubernetes
  • Experience working with Spark/PySpark to write and/or refactor Feature Engineering and Model Inference workflows
  • Experience building ETL Pipelines for ML Model Data Ingestion
  • Experience building automated Model Training/Retraining and Validation Pipelines
  • Hands on experience with and working knowledge of a wide range of standard Machine Learning Model variants, such as Regression, MLP's, Convolutional NN's, Classification and Regression Trees, Matrix Factorization, and Time Series Feature Engineering techniques such as FB Prophet
  • Experience working with Databricks Delta Lake to create and manage Delta Tables
  • Experience working with a variety of relational SQL and NoSQL databases
  • Experience requisitioning, creating, and debugging job flows on clusters
  • Experience with deployment technologies in one or more Cloud Providers (preferably AWS or GCP)
  • Experience scheduling cloud hosted workflows using tools like Cron or Apache Airflow
  • Experience specifying infrastructure to be built using tools such as Terraform or Jenkins
  • Expertise in designing and running unit tests on production code
  • Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
  • Experience working with distributed systems, service oriented architectures and designing APIs

We offer:

  • Opportunity to work on bleeding-edge projects
  • Work with a highly motivated and dedicated team
  • Competitive salary
  • Flexible schedule
  • Medical insurance
  • Benefits program
  • Corporate social events


Placement and Staffing Agencies need not apply. We do not work with C2C at this time.

About us:

Grid Dynamics is the engineering services company known for transformative, mission-critical cloud solutions for retail, finance and technology sectors. We architected some of the busiest e-commerce services on the Internet and have never had an outage during the peak season. Founded in 2006 and headquartered in San Ramon, California with offices throughout the US and Eastern Europe, we focus on big data analytics, scalable omnichannel services, DevOps, and cloud enablement.

Don’t see the right opportunity?

Contact us anyway and let’s talk! To apply, send your resume and cover letter to moc.scimanyddirg@sboj

Grid Dynamics is an equal opportunity employer. We are committed to creating an inclusive environment for all employees during their employment and for all candidates during the application process. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on, age, race, gender, color, religion, national origin, sexual orientation, gender identity, veteran status, disability or any other protected category. All employment is decided on the basis of qualifications, merit, and business need.

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