Machine Learning Engineer

Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team.

As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.

What You’ll Do:

• Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams
• Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems
• Lead large-scale ML initiatives with the customer in mind
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
• Optimize data pipelines to feed ML models
• Use programming languages like Python, Scala, Java, and Go Lang
• Leverage compute technologies such as Dask and RAPIDS
• Evangelize best practices in all aspects of the engineering and modeling life cycles
• Help recruit, nurture, and retain top engineering talent

Basic 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 8 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, Num Py, Scikit-learn)
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
• At least 5 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 Machine Learning software systems

Preferred Qualifications:

• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
• 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference.
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 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 (under fitting, overfitting)
• 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• Ability to communicate complex technical concepts clearly to a variety of audiences
• Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents
• Experience developing high-performing ML engineers with an inspiring leadership style

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

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.