Lead Machine Learning Engineer

4 days ago

New York, New York, United States Capital One Full time
Capital One Machine Learning Engineer

As a key member of our Agile team, you'll be responsible for productionizing machine learning applications and systems at scale. You'll participate in the technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. Your focus will be on machine learning architectural design, developing and reviewing model and application code, and ensuring high availability and performance of our machine learning applications.

Team Description

Our team is at the forefront of GenAI, driving the vision for AI at Capital One. The work of the AI Training Team touches every aspect of the model development life cycle, with deployed models in production driving business impact with visibility from our C-Suite.

  • Our team creates high-quality data for training and testing GenAI models, ensuring it's created, what's in those datasets, and the impact they have.
  • We're invested in building capabilities for evaluating and monitoring generative models, making them state-of-the-art, easy to use, and trusted by our users and contributors.
  • Horizontal capabilities enable vertical use case work, with the team building search, summarization, RAG, and agentic workflows for integration in production applications across the company.
Key Responsibilities

You'll be expected to perform many ML engineering activities, including:

  • Designing, building, and/or delivering ML models and components that solve real-world business problems, working in collaboration with the Product and Data Science teams.
  • Informing 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.
  • Solving complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
  • Collaborating as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
  • Retraining, maintaining, and monitoring models in production.
  • Leveraging or building cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
  • Constructing optimized data pipelines to feed ML models.
  • Leveraging continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
  • Ensuring 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.
  • Using programming languages like Python, Scala, or Java.
Basic Qualifications
  • Bachelor's degree
  • At least 6 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 2 years of experience building, scaling, and optimizing ML systems
Preferred Qualifications
  • 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
  • 2+ years of people leader experience
  • 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

Capital One does not provide, endorse, nor guarantee, and is not liable for third-party products, services, educational tools, or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe, and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

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