Capital One
Staff Machine Learning Engineer
$245K–$307K
Plano, TX +1 · Staff · Full-time · Posted Sep 29, 2026
- Location
- Plano, TX
McLean, VA - Experience
- 8+ years programming; 6+ years machine learning and distributed systems; 5+ years production ML and cloud operations. Preferred: 3+ years architecting ML systems, 5+ years optimizing ML, 5+ years ML techniques, and 7+ years production ML data pipelines.
- Education
- Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field; a Master's or Doctoral Degree is preferred.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Leads the development, deployment, and scaling of production machine-learning systems that address financial-services business problems.
What you'll do
- Deliver and evolve ML models and software, leading large-scale initiatives with customer needs in mind.
- Scale models using cloud architectures, optimize data pipelines, and promote engineering and modeling best practices.
- Help recruit, develop, and retain engineering talent.
What they ask for
- Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field.
- At least 8 years programming with Python, Java, Golang, or C++.
- At least 6 years of machine-learning experience with PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn.
- At least 6 years operating large-scale distributed systems to prepare ML data, and at least 5 years deploying production ML solutions and cloud services using Kubernetes.
Nice to have
- Master's or Doctoral Degree in a related field; experience architecting, optimizing, and scaling production ML systems and data pipelines.
- Experience with ML techniques and architectures, communicating technical concepts, industry contributions, or developing ML engineers.
Pay
$269,100 - $307,200 USD base salary; $244,700 - $279,200 USD base salary
Tools
Python, Scala, Java, GoLang, Dask, RAPIDS, PyTorch, Tensorflow
AI focus
Develops, deploys, optimizes, and scales production machine-learning models and data pipelines.
Model-written summary · the original posting is authoritative