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.
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Hybrid Senior Lead, Machine Learning Engineer – Enterprise Platform Technology
Posted 5 months ago
About the role
- Senior Lead Machine Learning Engineer at Capital One responsible for designing and implementing scalable machine learning applications. Collaborating in an Agile team to optimize and maintain production-ready models.
Responsibilities
- 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
- Focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance
- 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
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures to deliver optimized ML models
- Construct optimized data pipelines to feed ML models
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed, and follow best practices in AI
Requirements
- Bachelor’s degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing
- 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
- Comprehensive and competitive health benefits
- Financial and other benefits that support total well-being
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