Senior Machine Learning Engineer developing deep learning models for camera - based perception in autonomous trucks. Building robust camera models for safe and reliable autonomous driving.
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Hybrid Lead Machine Learning Engineer – MLOps, KServe, Kubernetes, PyTorch, TensorFlow, AWS
Posted 5 hours ago
About the role
- Lead Machine Learning Engineer at Capital One focused on machine learning applications and systems. Collaborate with Agile teams to develop scalable solutions for business problems.
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 machine learning applications
- Continuously learn and apply the latest innovations and best practices in machine learning engineering
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Inform ML infrastructure decisions using 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 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, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
Requirements
- Bachelor’s degree
- At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Scala, or Java
- At least 2 years of experience building, scaling, and optimizing ML systems
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field (Preferred)
- 3+ years of experience building production-ready data pipelines that feed ML models (Preferred)
- 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (Preferred)
- 2+ years of experience developing performant, resilient, and maintainable code (Preferred)
- 2+ years of experience with data gathering and preparation for ML models (Preferred)
- 2+ years of people leader experience (Preferred)
- 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation (Preferred)
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform (Preferred)
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance (Preferred)
Benefits
- performance based incentive compensation
- cash bonus(es)
- long term incentives (LTI)
- comprehensive, competitive, and inclusive set of health, financial and other benefits
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