Locations: NY – New York, United States of America, New York, New York

Senior Machine Learning Engineer

As a Capital One Machine Learning Engineer, you’ll be part of an Agile team dedicated toproductionizing machine learning applications and systems at scale. Youll participate in thedetailed technical design, development, and implementation of machine learning applicationsusing existing and emerging technology platforms. Working within an Agile environment, youllserve as a technical lead, providing input into machine learning architectural design decisions,developing and reviewing model and application code, and ensuring high availability andperformance of our machine learning applications. You’ll have the opportunity to continuouslylearn and apply the latest innovations and best practices in machine learning engineering. Youllalso mentor other engineers and further develop your technical knowledge and skills to keepCapital One at the cutting edge of technology.

We are seeking Software Engineers who are passionate about marrying data with emerging technologies to join our team. As a Capital One Software Engineer, youll have the opportunity to be on the forefront of driving a major transformation within Capital One. Learn more about #lifeatcapitalone and our commitment to diversity & inclusion by jumping to slides 76-91 on our Corporate Social Responsibility Report. This requisition is an advertisement for multiple opportunities within our Tech organization. By applying to this particular role, you’ll also be considered for other roles within Capital One’s Engineering Organization.

What youll do in the role:

  • Deliver ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.

  • 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, next generation big data and machine learning applications.

  • Leverage cloud-based architectures and technologies to deliver optimized ML models atscale.

  • Construct optimized data pipelines to feed ML models.

  • Use programming languages like Python, Scala, or Java.

  • Leverage continuous integration and continuous deployment best practices, includingtest automation and monitoring, to ensure successful deployment of ML models andapplication code.

Basic Qualifications :

  • Bachelors degree.

  • At least 3 years of experience designing and building data-intensive solutions using distributed computing.

  • At least 3 years of experience programming with Python, Scala, or Java.

  • 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:

  • At least 1 year of experience building, scaling, and optimizing ML systems.

  • At least 1 year(s) of experience with data gathering and preparation for ML models.

  • At least 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.

  • At least 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.

  • At least 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.

At this time, Capital One will not sponsor a new applicant for employment authorization for this position.

Tagged as: agile, AI, Azure, C++, Data, Go, Java, Machine Learning, Python, R, Scala, Spark, Tensorflow

Source:

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