Lead ML Engineer - Production-Scale & Cloud Platforms

Capital One; Plano (TX); On-site; USD 245,000 - 279,000; Full time

Job summary

Capital One in Plano, TX, seeks a senior Machine Learning Engineer to provide technical leadership for productionizing ML applications at scale. You will design, develop, and review model and application code with Product, Architecture, Engineering and Data Science teams.

You will mentor engineers, guide ML architecture decisions, leverage cloud architectures, optimize data pipelines, and evangelize best practices across the ML lifecycle to deliver high-availability systems.

Qualifications

• Bachelor’s degree required.
• 10+ years of experience designing and building data-intensive solutions using distributed computing.
• 6+ years of experience programming in C/C++, Python, or Scala.
• 3+ years of experience with the full ML development lifecycle in a business-critical setting.

Responsibilities

• Deliver ML models and software components that solve business problems in financial services, collaborating with product, architecture, engineering and data science teams.
• Drive creation and evolution of ML models and software for state-of-the-art intelligent systems.
• Lead large-scale ML initiatives with the customer in mind.
• Leverage cloud-based architectures to deliver optimized ML models at scale.
• Optimize data pipelines to feed ML models.
• Advocate best practices across the ML lifecycle and engineering.
• Help recruit and mentor top engineering talent.

Skills

Distributed computing

C/C++/Python/Scala

ML model deployment

Technical leadership

Education

Bachelor's degree

Master's degree

Tools

Dask

RAPIDS

NumPy

Pandas

Scikit-learn

Job description

Capital One in Plano, TX, seeks a senior Machine Learning Engineer to provide technical leadership for productionizing ML applications at scale. You will design, develop, and review model and application code with Product, Architecture, Engineering and Data Science teams.

You will mentor engineers, guide ML architecture decisions, leverage cloud architectures, optimize data pipelines, and evangelize best practices across the ML lifecycle to deliver high-availability systems.