Machine Learning Engineer 4 - Machine Learning Foundations (MLX)
$179k - $205k • San Jose, CA • Full-time
Posted 8d 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 Machine Learning Foundations (MLX) 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 leveraging cutting-edge open-source frameworks, advanced algorithms, and emerging technologies in a fast-paced, collaborative environment.
Responsibilities
- Design, build, and deliver ML models and components to solve business problems.
- Inform ML infrastructure decisions based on understanding of ML techniques and issues.
- Solve complex problems by writing and testing application code, developing ML models, and automating tests and deployment.
- Collaborate with cross-functional Agile teams to create and enhance 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.
- Apply CI/CD best practices, including test automation and monitoring, for successful deployment of ML models and code.
- Ensure code is well-managed for security, models are governed for risk, and ML practices follow Responsible and Explainable AI guidelines.
- Use programming languages like Python, Scala, or Java.
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 PyTorch or Tensorflow and libraries like 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) using Kubernetes.
Benefits
- Performance-based incentive compensation, including cash bonus(es) and/or long-term incentives (LTI).