Machine Learning Engineer 5

Capital One is seeking Machine Learning Engineers to design, build, and deliver production ML capabilities across large-scale platforms.

Responsibilities

• Design, build, and/or deliver ML models and components to address real-world business problems in partnership with Product and Data Science teams
• Build and scale large multi-tenant ML platforms to support high-footprint model training and/or serving at scale
• Apply ML modeling knowledge to guide ML infrastructure decisions, 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
• Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Construct optimized data pipelines that feed ML models
• Use CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
• Manage code to reduce vulnerabilities, ensure risk-governed models, and follow best practices for 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 Machine Learning experience using industry-standard frameworks PyTorch or TensorFlow plus libraries (Pandas, NumPy, Scikit-learn)
• At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
• At least 4 years of experience deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), including Kubernetes to manage containerized ML systems at scale

Technologies

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

Benefits

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

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 applying 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 accuracy and diagnosing issues such as 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 varied audiences

Location: McLean, VA (onsite)

Compensation: USD 229,900 - 262,400 per year (Machine Learning Engineer 5)

• Expected to accept applications for a minimum of 5 business days
• No agencies please
• Equal Opportunity Employer (EOE, including disability/vet), committed to non-discrimination per applicable laws
• Drug-free workplace
• Capital One considers applicants with criminal history in a manner consistent with applicable laws
• Accommodation request contact: 1-800-304-9102 or [email protected]
• Technical support/questions about recruiting process: [email protected]
• Capital One does not endorse or guarantee third-party products or information available through this site
• Posting location may correspond to Capital One Canada, Capital One Europe, or Capital One Philippines Service Corp. (COPSSC)