Senior Machine Learning Engineer
3 weeks ago
About the Role
As a Senior Machine Learning Engineer at Capital One, you will be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. Your primary responsibility will be to design, build, and deliver machine learning models and components that solve real-world business problems, working closely with the Product and Data Science teams.
Key Responsibilities
- Design, build, and deliver machine learning models and components that solve real-world business problems.
- Inform your ML infrastructure decisions using your 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 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.
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 an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
- At least 1 year of experience productionizing, monitoring, and maintaining models
Preferred Qualifications
- 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 such as 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
What We Offer
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
Equal Employment Opportunity
Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex (including pregnancy, childbirth or related medical conditions), race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, gender reassignment, citizenship, immigration status, protected veteran status, or any other basis prohibited under applicable federal, state or local law.
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