Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology)
$197k - $225k • 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 Enterprise Platforms Technology team. This role involves leveraging cutting-edge open-source frameworks, advanced algorithms, and emerging technologies to drive AI transformations and scale production models. You will collaborate with Product and Data Science teams to design, build, and deliver ML models that solve real-world business problems. The position requires a strong understanding of ML infrastructure, modeling techniques, and the ability to write and test application code, automate deployments, and ensure responsible AI practices.
Responsibilities
- Design, build, and deliver ML models and components to solve business problems.
- Inform ML infrastructure decisions based on 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 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.
- Leverage CI/CD best practices, including test automation and monitoring, for ML models and application code deployment.
- Ensure code is well-managed for security, models are governed for risk, and ML follows Responsible and Explainable AI best practices.
- 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 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 production services in the cloud (AWS, GCP, Azure).
- Experience using Kubernetes to manage large-scale containerized ML software systems.
- Experience optimizing ML algorithms, configurations, and infrastructure.
- Experience following software development best practices including source control, testing, code reviews, CI/CD.
- Experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
- Experience with Machine Learning techniques (Supervised, semi-supervised, unsupervised, reinforcement learning), model types (Regression, Classification, Clustering), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts, and model evaluation.
- Experience designing, implementing, and scaling production-ready data pipelines for ML models.
- Experience as a technical lead developing ML solutions using industry best practices, patterns, and automation.
- Authored/co-authored a paper on an ML technique, model, or proof of concept.