As a Senior Machine Learning Engineer, you will be operationalizing and deploying machine learned models that improve the relevance and discoverability of what customers see when they use our Client’s services. The framework you define will lay the foundation for a seamless pipeline that decreases the cost of launching ML models to increase the throughput of experimentation and experience optimization. The optimal skill set for this role spans software engineering and machine learning, bridging the gap between modeling and application.
- Work with data science teams to create machine learned models and building a framework to quickly bring those models and their subsequent iterations into production
- Bring models to production and enable others to do the same
- Work with software engineering teams while working cross-functionally with product, design, and data science teams
- Assist in the prioritization of where they apply models to business problems.
- Stay up to date on industry trends around applied machine learning and how they apply to our problem space
- 5 years of experience in Machine Learning operationalization, and 10 years of experience in software engineering
- Experience working as part of a software engineering team
- Experience building machine learning systems for production applications
- Ability to use statistical methodologies to ensure we have sufficient statistical power to accurately evaluate results.
Nice to Have:
- A degree in computer science, statistics, engineering, or mathematics
- Experience with machine learning/deep learning frameworks/libraries like AWS SageMaker, TensorFlow, PyTorch, MXNet, or related.
- A background in handling big data via open-source tools (e.g. Spark, Hadoop) or using 3rd party platforms that can convert data sets into high quality models