Principal Machine Learning Engineer leading AI and Machine Learning systems at Bumble for recommendations and personalization. Driving improvements in user engagement and safety across Bumble products.
About the role
- Join Capital One as a Machine Learning Engineer, working on productionizing ML applications. Focus on designing, building, and maintaining robust ML infrastructures and solutions.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems
- Inform ML infrastructure decisions using understanding of ML modeling techniques and issues
- 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 for ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage CI/CD best practices to ensure successful deployment of ML models
- Ensure all code is well-managed to reduce vulnerabilities and governance of models for risk
- Use programming languages like Python, Scala, or Java
Requirements
- Bachelor’s Degree
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience designing and building data-intensive solutions using distributed computing
- At least 2 years of on-the-job experience with industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
- At least 1 year of experience productionizing, monitoring, and maintaining models
- 1+ years of experience building, scaling, and optimizing ML systems
- 1+ years of experience with data gathering and preparation for ML models
- 2+ years of experience developing performant, resilient, and maintainable code
- Experience developing and deploying ML solutions in a public cloud (AWS, Azure, or Google Cloud Platform)
- Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
- 3+ years of experience with 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
- 3+ years of experience building production-ready data pipelines that feed ML models
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Benefits
- Comprehensive health benefits
- Competitive bonuses
- Performance-based incentives
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