Machine Learning Engineer 4 - Intelligent Foundations and Experiences (IFX)
$215k - $246k • New York, NY • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Capital One is seeking passionate Machine Learning Engineers to join their Intelligent Foundations and Experiences (IFX) team. In this role, you will be at the forefront of driving AI transformations and scaling production models across the company. You will collaborate with Product and Data Science teams to design, build, and deliver ML models that solve real-world business problems. This position involves working in a fast-paced, collaborative environment, leveraging cutting-edge open-source frameworks, advanced algorithms, and emerging technologies.
Responsibilities
- Design, build, and deliver ML models and components to solve business problems.
- Inform ML infrastructure decisions based on understanding of ML modeling techniques.
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Collaborate with cross-functional Agile teams to create and enhance software for big data and ML applications.
- Retrain, maintain, and monitor models in production.
- Leverage or build cloud-based architectures and platforms for optimized ML model delivery at scale.
- Construct optimized data pipelines to feed ML models.
- Implement continuous integration and continuous deployment (CI/CD) best practices for ML models and application code.
- Ensure code security, model governance, and adherence to Responsible and Explainable AI best practices.
Requirements
- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- 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 like PyTorch or Tensorflow and libraries such as Pandas, NumPy, Scikit-learn.
- At least 4 years of experience using and operating large-scale distributed systems (Spark, Ray) for AI/ML data preparation.
- At least 2 years of experience deploying and operating ML solutions in production and cloud environments (AWS, GCP, Azure).
- Experience using Kubernetes to manage large-scale containerized ML software systems.
- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred).
- 3+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred).
- 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD (preferred).
- 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, and incident response planning (preferred).
- 3+ years of experience with various Machine Learning techniques, model types, architectures, training concepts, and evaluation methods (preferred).
- 3+ years of experience designing, implementing, and scaling production-ready data pipelines for ML models (preferred).
- 1+ years of experience as a technical lead developing ML solutions (preferred).
- Authored/co-authored a paper on an ML technique, model, or proof of concept (preferred).
Benefits
- Performance-based incentive compensation (cash bonus(es) and/or long-term incentives).
- Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.