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About the Role
Capital One is seeking Machine Learning Engineers passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join their team. In this role, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Inform ML infrastructure decisions using understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model 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 as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art 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, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the 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 3 years of experience programming with Python, Java, Golang, or C++
- At least 2 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
- At least 3 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data
- At least 1 year 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
- 1+ years of experience optimizing ML algorithms, configurations, and infrastructure
- 1+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
- 1+ 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
- 1+ 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)
- Authored/co-authored a paper on a ML technique, model, or proof of concept
- 1+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Skills
- Python
- Java
- Golang
- C++
- PyTorch
- Tensorflow
- Pandas
- NumPy
- Scikit-learn
- Spark
- Ray
- AWS
- GCP
- Azure
- Kubernetes
- Machine Learning
- Data Engineering
- CI/CD
- Responsible AI
- Explainable AI
Location
- McLean, VA
- New York, NY
- Plano, TX
- Richmond, VA
Work Type
- Full-time
Experience Level
- 3+ years of experience programming
- 2+ years of Machine Learning experience
- 3+ years of experience using and operating large scale distributed systems
- 1+ year of experience deploying and operating Machine Learning solutions in production
- 1+ years of experience optimizing ML algorithms
- 1+ years of experience following software development best practices
- 1+ years of experience building resilient software solutions
- 1+ years of experience working with Machine Learning techniques
- 1+ years of experience designing, implementing, and scaling production-ready data pipelines
Education Level
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field
- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
Salary/Compensations
- McLean, VA: $161,800 - $184,600
- New York, NY: $176,500 - $201,400
- Plano, TX: $147,100 - $167,900
- Richmond, VA: $147,100 - $167,900
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.