Machine Learning Engineer 5 (Senior Manager, IC)

Capital One • San Francisco, NY

1d • $229,900 - $286,200

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production models. The role brings together machine learning engineering, operations, modeling, and data engineering to build solutions for customer and business needs in a collaborative, iterative environment.

The engineer designs and delivers machine learning models and components, builds scalable multi-tenant platforms for model training and serving, and makes infrastructure decisions informed by modeling techniques and data needs. The work also includes developing and testing application code, automating tests and deployments, collaborating with Product, Data Science, and Agile teams, maintaining and monitoring production models, building cloud-based architectures and data pipelines, and applying responsible AI, model governance, and security practices. Programming languages used may include Python, Scala, and Java.

The position is full-time and offers location-dependent annual salaries: $229,900–$262,400 in Cambridge, MA and McLean, VA, and $250,800–$286,200 in New York, NY, San Francisco, CA, and San Jose, CA. The role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Capital One states that it offers a comprehensive benefits package; eligibility varies by employment status and management level.

Requirements

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field.
• At least 6 years of programming experience with Python, Java, Golang, or C++.
• At least 6 years of machine learning experience using PyTorch or TensorFlow and libraries such as Pandas, NumPy, and Scikit-learn.
• At least 6 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or machine learning data.
• At least 4 years of experience deploying and operating machine learning solutions in production and operating production services in the cloud (AWS, GCP, or Azure), including use of Kubernetes to manage large-scale containerized machine learning systems.

Nice To Haves

• Master’s or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• 5 or more years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• 5 or more years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 5 or more years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• 5 or more years of experience with machine learning techniques, model types and architectures, training concepts, model evaluation, and diagnosing issues such as underfitting and overfitting.
• 5 or more years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• Machine learning industry impact through conference presentations, papers, blog posts, open-source contributions, or patents.
• Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences.

Responsibilities

• Design, build, and deliver machine learning models and components to address business problems, collaborating with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-scale machine learning training and serving.
• Make machine learning infrastructure decisions informed by modeling techniques, data and feature selection, training, tuning, dimensionality, bias and variance, and validation.
• Write and test application code, develop and validate machine learning models, and automate testing and deployment.
• Collaborate with cross-functional Agile teams to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized models at scale.
• Construct data pipelines to feed machine learning models.
• Apply continuous integration and continuous deployment practices, including test automation and monitoring.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI practices.
• Use programming languages such as Python, Scala, or Java.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior