Machine Learning Engineer 4 - Machine Learning Experiences (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 Machine Learning Engineers passionate about leveraging cutting-edge open-source frameworks, advanced algorithms, and emerging technologies to drive AI transformations and scale production models. In this role, you will be part of a collaborative team focused on solving complex business problems in a fast-paced, iterative environment. You will have the opportunity to design, build, and deliver ML models and components, inform ML infrastructure decisions, and automate tests and deployment.
Responsibilities
- Design, build, and deliver ML models and components to solve business problems in collaboration with Product and Data Science teams.
- Inform ML infrastructure decisions based on understanding of 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 within a cross-functional Agile team 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 to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models.
- Leverage CI/CD best practices, including test automation and monitoring, for successful deployment of ML models and application code.
- Ensure code is well-managed for reduced vulnerabilities, models are well-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 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 or Machine Learning data preparation.
- At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) using Kubernetes.
- 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 Machine Learning techniques, model types, architectures, training concepts, and model evaluation (preferred).
- 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred).
- 1+ years of experience as a technical lead developing ML solutions using industry best practices and automation (preferred).
- Authored/co-authored a paper on an ML technique, model, or proof of concept (preferred).
Benefits
- Performance based incentive compensation