Senior Lead Machine Learning Engineer

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: McLean, VASan Francisco, CACambridge, MASan Jose, CANew York, NY
Employment: Full-time
Posted: 53 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $229k

$173k $289k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $203,900–$254,750.

Based on 240 similar postings.

Employer

About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

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At a glance

TL;DR · Senior Lead Machine Learning Engineer

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The Senior Lead Machine Learning Engineer joins an Agile team focused on productionizing machine learning applications and systems at scale. This role involves participating in detailed technical designs, developing and reviewing model and application code, and ensuring high availability and performance. Key responsibilities include building ML models to solve real-world business problems, constructing optimized data pipelines, and automating tests and deployments. The engineer will retrain, maintain, and monitor models in production while ensuring they follow best practices for Responsible and Explainable AI. Required technical skills include proficiency in Python, Scala, or Java, along with experience in distributed computing and ML frameworks like scikit-learn, PyTorch, Dask, Spark, or TensorFlow. The work involves building cloud-based architectures to deliver optimized models and managing data-intensive solutions.

Skills

Python Scala Java PyTorch TensorFlow Spark Dask scikit-learn AWS Azure Google Cloud Platform Distributed Computing Data Pipelines Machine Learning Agile Explainable AI

What you'll do

Design, build, and deliver machine learning models and components to solve real-world business problems.
Develop and validate application code while automating tests and deployment processes.
Retrain, maintain, and monitor machine learning models in production environments.
Construct optimized data pipelines to feed machine learning models.
Build and leverage cloud-based architectures to deliver optimized ML models at scale.
Implement continuous integration and continuous deployment best practices for model deployment.
Ensure all code and models follow best practices for Responsible and Explainable AI.

What we're looking for

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 experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred).