Senior Lead Machine Learning Engineer

Capital One Financial

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: New York, NYSan Francisco, CAMcLean, VACambridge, MASan Jose, CA
Employment: Full-time
Posted: 137 days ago
Freshness: Confirmed live yesterday
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 $202,800–$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 Sr. Lead Machine Learning Engineer joins an Agile team focused on productionizing machine learning applications and systems at scale. This role involves the technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. Key responsibilities include building and delivering ML models to solve real-world business problems, constructing optimized data pipelines, and maintaining models in production. The engineer will automate tests and deployments while ensuring high availability, performance, and adherence to Responsible and Explainable AI practices. 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, Kubeflow, or TensorFlow. The work involves solving complex problems by developing performant, resilient code within cloud-based architectures.

Skills

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

What you'll do

Design, build, and deliver machine learning models and components to solve real-world business problems.
Develop and review application code using Python, Scala, or Java to ensure high availability and performance.
Construct and optimize data pipelines to feed machine learning models.
Retrain, maintain, and monitor machine learning models in production environments.
Build and leverage cloud-based architectures and technologies to deliver optimized models at scale.
Implement continuous integration and continuous deployment best practices, including test automation and monitoring.
Ensure all models follow best practices in Responsible and Explainable AI and meet risk governance standards.

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, Kubeflow or TensorFlow (preferred).

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