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We're looking for a Lead - Machine Learning Engineer 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 automating governance within the model development lifecycle. You will work with model training and feature and serving metadata at scale, to enable automated model governance decisions. 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 decision-making.

Responsibilities :

- Work with model and platform teams to build systems that ingest large amounts of model and feature metadata that will feed into automated governance decisions.

- Partner with product and design teams to build elegant and scalable solutions to speed up governance processes.

- 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 at scale.

- Construct optimized data pipelines to feed machine-learning models.

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

- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of machine learning models and application code.

- Advocate for software and machine learning engineering best practices.

- Function as a technical lead.

Requirements :

- 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, Go, or Java.

- At least 2 years of experience building, scaling, and optimizing ML systems.

- At least 2 years of experience with the full ML Development Lifecycle using industry-recognized best practices.

- Master's Degree or PhD in Computer Science, Electrical Engineering, Mathematics, or a similar field.

- Atleast 3 years of experience in building production-ready data pipelines that feed ML models.

- Atleast 3 years of job experience with an industry-recognized ML framework such as sci-kit-learn, PyTorch, Dask, Spark, or TensorFlow.

- Atleast 2 years of experience developing performant, resilient, and maintainable code.

- Atleast 2 years of experience with data gathering and preparation for ML models, practices, patterns, and automation.

- Candidates should have experience with Model Observability - Monitoring and Telematics, MLOps, Python Scripting, Stats and Mathematical Modelling, and Data Pipelines.