Machine Learning Engineer 5

Build and operate machine learning models and platforms that deliver production impact for real business use cases.

Responsibilities

• Design, build, and deliver ML models and components in collaboration with Product and Data Science teams to solve real-world business problems
• Build and scale multi-tenant platforms for large-footprint ML training and/or model serving
• Drive ML infrastructure decisions using expertise in model choice, data and feature selection, training approach, hyperparameter tuning, dimensionality, bias-variance tradeoffs, 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 team to create and enhance software for state-of-the-art big data and ML applications
• Leverage and/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 deployment of ML models and application code
• Maintain well-governed code to reduce vulnerabilities; ensure responsible governance from a risk perspective; follow best practices in Responsible and Explainable AI
• Use programming languages such as Python, Scala, or Java

Requirements

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 6 years of experience programming with Python, Java, Golang, or C++
• At least 6 years of ML experience with industry frameworks and libraries: PyTorch or Tensorflow plus Pandas, NumPy, Scikit-learn
• At least 6 years experience using and operating large-scale distributed systems (for preparing AI/ML data): Spark, Ray
• At least 4 years deploying and operating ML solutions in production and operating production services in cloud environments (AWS, GCP, Azure) with Kubernetes for containerized ML systems

Technologies

• Python, Scala, Java, Golang, C++
• PyTorch, Tensorflow
• Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes
• CI/CD (continuous integration, continuous deployment)

Benefits

• Eligible for performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Preferred Qualifications

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or related field
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD
• 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation
• 5+ years of experience with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy while diagnosing and addressing underfitting and overfitting
• 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
• ML 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

Location

• Richmond, VA (onsite)

Salary

• USD 209,000 - 238,500 per yearly

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries.

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