Machine Learning Engineer 4 - Machine Learning Experiences (MLX)

Build and deploy ML experiences at scale. Capital One’s Machine Learning Experiences team develops and releases machine learning models and components that solve real business needs, supported by open-source frameworks and advanced algorithms. This onsite role in McLean, VA supports end-to-end machine learning engineering, from modeling and data to infrastructure, deployment, and responsible AI governance.

What you’ll do

• Design, build, and/or deliver ML models and components in collaboration with Product and Data Science teams.
• Drive ML infrastructure choices based on modeling fundamentals, including model selection, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Work in a cross-functional Agile environment to create and enhance software for state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor production models.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines that feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to support successful deployments of ML models and application code.
• Manage code to reduce vulnerabilities, ensure models are governed from a risk perspective, and follow best practices for Responsible and Explainable AI.
• Use programming languages such as Python, Scala, or Java.

Skills and qualifications

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
• At least 4 years of experience programming with Python, Java, Golang, or C++.
• At least 4 years of Machine Learning experience using industry-standard frameworks PyTorch or TensorFlow, and libraries such as Pandas, NumPy, and Scikit-learn.
• At least 4 years operating large-scale distributed systems (such as Spark and Ray) to prepare AI or Machine Learning data.
• At least 2 years deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure), including using Kubernetes to manage large-scale containerized ML systems.

Technologies

Python, Scala, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn, Spark, Ray, AWS, GCP, Azure, Kubernetes

Preferred qualifications

• Master’s or Doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• 3+ years optimizing ML algorithms, configurations, and infrastructure.
• 3+ years following software development best practices (source control, testing, code reviews, CI/CD, etc.).
• 3+ years building resilient software with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation.
• 3+ years applying ML techniques (supervised, semi-supervised, unsupervised, reinforcement learning), model types (regression, classification, clustering), architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and diagnosing issues such as underfitting and overfitting.
• 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• 1+ years experience serving as a technical lead for ML solutions using industry best practices, patterns, and automation.
• Authored or co-authored a paper on a ML technique, model, or proof of concept.

Compensation and work details

Salary (McLean, VA): USD 197,300 - 225,100 per year

This role may also be eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

Location: McLean, VA (onsite)

Experience level: Minimum 2 years

Application timeline: Expected to accept applications for a minimum of 5 business days.

Additional information

• No agencies please.
• Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable laws.
• Capital One promotes a drug-free workplace.
• Capital One will consider qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries.
• Accommodation requests for applying: contact Capital One Recruiting at 1-800-304-9102 or [email protected].
• For technical support or questions about recruiting: [email protected].
• Capital One will not sponsor a new applicant for employment authorization, and does not offer immigration-related support for this position (examples include H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1).