Machine Learning Engineer focusing on MLOps and software engineering at flaschenpost, ensuring robust planning and operational success through ML products.
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Hybrid Senior Lead, Machine Learning Engineer – Enterprise Platform Technology
Posted 5 months ago
About the role
- Senior Lead Machine Learning Engineer involved in the technical design, development, and implementation of ML applications. Part of an Agile team at Capital One optimizing and scaling ML solutions.
Responsibilities
- Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
- Focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
- Perform ML engineering activities, including design, build, and/or deliver ML models and components that solve real-world business problems.
- Inform ML infrastructure decisions using knowledge 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 as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
- Retrain, maintain, and monitor models in production.
- Leverage or build cloud-based architectures to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models.
- Leverage continuous integration and continuous deployment best practices to ensure successful deployment of ML models and application code.
- Ensure all code is well-managed to reduce vulnerabilities, and that models are well-governed from a risk perspective.
Requirements
- Bachelor’s degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field (preferred)
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (preferred)
- 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred)
- 3+ years of experience developing performant, resilient, and maintainable code (preferred)
- 3+ years of experience with data gathering and preparation for ML models (preferred)
- 3+ years of people management experience (preferred)
- 3+ years of experience building production-ready data pipelines that feed ML models (preferred)
Benefits
- Health insurance
- Performance based incentive compensation
- Cash bonus(es)
- Long term incentives (LTI)
- Comprehensive and competitive benefits
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