Machine Learning Engineering - Intelligent Foundations and Experiences (ifx)

Capital One Capital One · Banking · Richmond, VA +1

Machine Learning Engineer focused on productionizing ML models and systems at scale within a fintech company. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing code, automating tests and deployment, retraining/maintaining/monitoring models in production, leveraging cloud architectures, constructing data pipelines, and ensuring code quality and model governance. The role emphasizes experience with ML frameworks, productionizing models, and cloud-based ML systems.

What you'd actually do

  1. Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
  2. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
  3. Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
  4. Retrain, maintain, and monitor models in production.
  5. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.

Skills

Required

  • Python
  • Scala
  • Java
  • designing and building data-intensive solutions using distributed computing
  • industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
  • productionizing, monitoring, and maintaining models

Nice to have

  • building, scaling, and optimizing ML systems
  • data gathering and preparation for ML models
  • developing performant, resilient, and maintainable code
  • developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
  • distributed file systems or multi-node database paradigms
  • Contributed to open source ML software
  • Authored/co-authored a paper on a ML technique, model, or proof of concept
  • building production-ready data pipelines that feed ML models
  • designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • productionizing, monitoring, and maintaining models
  • building, scaling, and optimizing ML systems
  • developing and deploying ML solutions in a public cloud

Other signals

  • productionizing machine learning applications and systems at scale
  • design, build, and/or deliver ML models and components
  • retrain, maintain, and monitor models in production
  • leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
  • construct optimized data pipelines to feed ML models