Machine Learning Engineer designing and implementing AI systems focused on Japanese language challenges at Woven by Toyota. Involves technical R&D, system design, and collaboration with cross - functional teams.
About the role
- Machine Learning Engineer at Capital One focusing on building and optimizing ML applications. Collaborating in Agile teams and applying latest innovations in machine learning engineering.
Responsibilities
- As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.
- Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
- Focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
- Continuously learn and apply the latest innovations and best practices in machine learning engineering.
- The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering.
- Perform many ML engineering activities, including one or more of the following: 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.
- 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).
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
- 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.
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
- Use programming languages like Python, Scala, or Java.
Requirements
- Bachelor’s degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field (Preferred)
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (Preferred)
- 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (Preferred)
- 3+ years of experience developing performant, resilient, and maintainable code (Preferred)
- 3+ years of experience with data gathering and preparation for ML models (Preferred)
- 3+ years of people management experience (Preferred)
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents (Preferred)
- 3+ years of experience building production-ready data pipelines that feed ML models (Preferred)
- Ability to communicate complex technical concepts clearly to a variety of audiences (Preferred).
Benefits
- Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
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