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About the Role
Leverage cutting-edge open-source frameworks, advanced algorithms, and emerging technologies to drive AI transformations and scale production models across Capital One. This role involves designing, building, and deploying ML models and platforms, optimizing ML infrastructure, and ensuring responsible AI practices.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems in collaboration with Product and Data Science teams.
- Build and scale massive multi-tenant platforms for large footprint ML model training and/or serving.
- Inform ML infrastructure decisions using understanding of ML modeling techniques and issues.
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Collaborate as part of 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-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models.
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, for successful deployment of ML models and application code.
- Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows best practices in Responsible and Explainable AI.
- Use programming languages like 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 and 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 Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems.
- 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, CI/CD, etc.
- 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 preparing incident response plans.
- 5+ years of experience working with Machine Learning 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 how to evaluate model accuracy and diagnose and address common issues (underfitting, 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.
Skills
- Python
- Java
- Golang
- C++
- PyTorch
- Tensorflow
- Pandas
- NumPy
- Scikit-learn
- Spark
- Ray
- AWS
- GCP
- Azure
- Kubernetes
- Scala
- Responsible AI
- Explainable AI
- CI/CD
- Test Automation
- Monitoring
- Supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Regression
- Classification
- Clustering
- RNNs
- CNNs
- LSTMs
- Transformers
Location
- Cambridge, MA
- McLean, VA
- New York, NY
- San Francisco, CA
- San Jose, CA
Work Type
- Full-time
Experience Level
- Senior
- 6+ years
- 4+ years
- 5+ years
Education Level
- Bachelor's Degree
- Master's Degree
- Doctoral Degree
Salary/Compensations
- Cambridge, MA: $229,900 - $262,400
- McLean, VA: $229,900 - $262,400
- New York, NY: $250,800 - $286,200
- San Francisco, CA: $250,800 - $286,200
- San Jose, CA: $250,800 - $286,200
Benefits
- Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
- Eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
About the Company
- Capital One is a group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs.
- Capital One Financial is made up of several different entities.
Equal Opportunity
- 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 the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
- If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.