Distinguished Engineer - Machine Learning Engineering

We’re looking for a Distinguished Engineer - Machine Learning Engineering to join the Machine Learning Experience (MLX) team! As a Capital One Machine Learning Engineer (MLE), you’ll be part of a team focusing on observability and model governance automation. You will work with model training and features and serving metadata at scale, to enable automated model governance decisions and to build a model observability platform. You will contribute to building a system to do this for Capital One models, accelerating the move from fully trained models to deployable model artifacts ready to be used to fuel business decisioning and build an observability platform to monitor the models and platform components.

• Work with model and platform teams to build systems that ingest large amounts of model and feature metadata and runtime metrics to build an observability platform and to make governance decisions.
• Partner with product and design teams to build elegant and scalable solutions to speed up model governance observability
• 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.

• Master's Degree in Computer Science or a related field
• At least 15 years of experience in software engineering or solution architecture
• At least 10 years of experience designing and building data intensive solutions using distributed computing
• At least 10 years of experience programming with Python, Go, or Java
• At least 8 years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow

• 401k matching
• Health insurance
• Flight privileges

At Capital One, we work in a fast-paced and intellectually rigorous environment to solve fundamental business problems at scale. Using advanced analytics, data science and machine learning, we derive valuable insights about product and process design, consumer behavior, regulatory and credit risk, and more from large volumes of data, and use it to build cutting-edge patentable products that drive the business forward.