About the position Interested in joining a dynamic remote first engineering team in a fast-paced environment full of greenfield problem-solving? Then Capital One Shopping might be the place for you. Join us in supporting a growth-stage line of business with a startup mindset as we build technology to save our customers money. As a Capital One Machine Learning Engineer (MLE), you'll be part of a fast moving, highly collaborative Agile team dedicated to productionizing machine learning applications and systems at scale. You'll drive and deliver the detailed technical designs, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll be a leader of machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll contribute to researching our next generation of models and recommendation systems to deliver value to our customers. You'll mentor junior developers and serve as a technical bridge between product partners. You will use tools like Docker, Nomad, SQL, Python, Pytorch, Transformers, language models, and other statistical tools. This is more than just a job; it's an opportunity to be part of a collaborative and forward-thinking community, where your contributions will make a significant impact in an ever-dynamic tech landscape. Join us as we push boundaries and redefine the future of our industry.

Responsibilities

• 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.
• Design and research new models using data scientist experience/expertise.

Requirements

• Bachelor's degree
• At least 6 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 2 years of experience building, scaling, and optimizing ML systems

Nice-to-haves

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 3+ years of experience building production-ready data pipelines that feed ML models
• 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 2+ years of experience developing performant, resilient, and maintainable code
• 2+ years of experience with data gathering and preparation for ML models

Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
• Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

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