FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Machine Learning Engineer 4, Python, AWS, SQL, GenAI
Capital One. Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams .
Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go
About the role
Key responsibilities & impact- Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
- Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Write and test application code, develop and validate ML models, and automate tests and deployment
- Collaborate in a cross-functional Agile team to create and enhance software enabling big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
- Construct optimized data pipelines feeding ML models
- Apply continuous integration and continuous deployment practices, including test automation and monitoring
- Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI best practices
- Use programming languages such as Python, Scala, or Java
Requirements
What you’ll need- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field
- At least 4 years of programming experience with Python, Java, Golang, or C++
- At least 4 years of Machine Learning experience using PyTorch or TensorFlow and Pandas, NumPy, or Scikit-learn
- At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI/ML data
- At least 2 years of experience deploying and operating Machine Learning solutions in production
- At least 2 years of experience operating production cloud services using AWS, GCP, or Azure
- At least 2 years of experience using Kubernetes to manage large-scale containerized ML software systems
- No new-applicant employment authorization sponsorship or immigration-related support available
- Preferred: Master's or Doctoral Degree in a related field
- Preferred: 3+ years optimizing ML algorithms, configurations, and infrastructure
- Preferred: 3+ years following software development best practices including source control, testing, code reviews, and CI/CD
- Preferred: 3+ years building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response planning
- Preferred: 3+ years working with ML techniques, model types, architectures, training concepts, and model evaluation
- Preferred: 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
- Preferred: 1+ years as a technical lead developing ML solutions
- Preferred: Authored or co-authored a paper on an ML technique, model, or proof of concept
Benefits
Comp & perks- Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
- Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
- Reasonable accommodations for applicants who require them