Machine Learning Engineer 4 - Finance Tech

Build and scale production AI and ML that supports real financial decisioning, including forecasting, treasury and trade, and accounting and reporting. This hybrid Machine Learning Engineering role on Capital One’s Finance Tech team focuses on the full lifecycle: modeling and data pipelines, deployment and monitoring, and Responsible AI governance.

What you’ll do

• Design, build, and/or deliver ML models and components to address real-world business problems in collaboration with Product and Data Science teams
• Shape ML infrastructure decisions using expertise across model selection, data and feature selection, training, 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 that enables big data and ML applications
• Retrain, maintain, and monitor production models
• Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to support ML model training and delivery
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases
• Help ensure code is well-managed to reduce vulnerabilities, and that models are well-governed from a risk perspective while following Responsible and Explainable AI best practices
• Use Python, Scala, or Java in day-to-day engineering work

Core 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, plus libraries including Pandas, NumPy, and Scikit-learn
• At least 4 years using and operating large scale distributed systems such as Spark and Ray to prepare AI or ML data
• At least 2 years deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), including using Kubernetes for large scale containerized ML systems

Technology stack

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 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 and alarms, and incident response plan preparation
• 3+ years working with ML techniques (supervised, semi-supervised, unsupervised, reinforcement learning) and model types (regression, classification, clustering), including architectures (RNNs, CNNs, LSTMs, Transformers) and evaluation and diagnosis (underfitting/overfitting)
• 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
• 1+ year as a technical lead developing ML solutions using industry best practices, patterns, and automation
• Authored/co-authored a paper on an ML technique, model, or proof of concept

Location and schedule

• Cambridge, MA (Hybrid)
• This role is designated as hybrid. You are expected to adhere to Capital One’s hybrid policy for required on-site presence at the designated office location.

Compensation and incentives

Base salary range: USD 197,300 - 225,100 per year.

• Cambridge, MA: $197,300 - $225,100
• McLean, VA: $197,300 - $225,100
• New York, NY: $215,200 - $245,600

This role is also eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI). Incentives could be discretionary or non-discretionary depending on the plan.

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being.

Additional notes

Capital One Financial is made up of several different entities. Any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe, and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).