Machine Learning Engineer IV

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: New York, NYSan Francisco, CAMcLean, VARichmond, VAPlano, TX
Salary: $197,300–$225,100 / yr
Posted: 3 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $217k

This role $211k

$172k most similar roles pay here $249k

This role pays more than 57% of similar roles. Most pay $192,050–$241,750 — the shaded band above. At the midpoint, this role pays about $211k versus about $217k for comparable roles.

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 836 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 829 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Machine Learning Engineer IV

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Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) joins the Marketing and Messaging team to build scalable platforms for delivering hyper-personalized omnichannel messages across owned and paid Adtech channels. In this role, you will design, build, and deliver machine learning models and components that solve real-world business problems while collaborating with Product and Data Science teams. You will be responsible for constructing optimized data pipelines, retraining and monitoring production models, and managing infrastructure decisions regarding feature selection and hyperparameter tuning. The role requires proficiency in Python, Scala, or Java, along with experience in PyTorch, TensorFlow, Pandas, NumPy, Scikit-learn, Spark, Ray, and Kubernetes. You will work within a cloud-based environment using AWS to develop robust software that supports large-scale big data applications while ensuring responsible and explainable AI practices.

What does a Machine Learning Engineer earn in New York?

Median $230775 from 44 postings across 15 companies.

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Skills

Python AWS SQL GenAI PyTorch TensorFlow Pandas NumPy Scikit-learn Spark Ray Kubernetes Scala Java C++ CI/CD Transformer

What you'll do

Design, build, and deliver machine learning models and components to solve real-world business problems.
Inform infrastructure decisions based on model training, feature selection, hyperparameter tuning, and validation techniques.
Write and test application code while automating tests and deployment processes for ML models.
Construct and optimize data pipelines to feed production-ready machine learning models.
Retrain, maintain, and monitor machine learning models in a live production environment.
Build and manage cloud-based architectures to deliver optimized models at scale.
Implement CI/CD best practices and monitoring to ensure successful deployment of code and models.
Ensure all models follow best practices for Responsible and Explainable AI and risk management.

What we're looking for

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field.
At least 4 years of experience programming with Python, Java, Golang, or C++.
At least 4 years of Machine Learning experience using PyTorch, TensorFlow, Pandas, NumPy, and Scikit-learn.
At least 4 years of experience operating large scale distributed systems like Spark or Ray to prepare AI/ML data.
At least 2 years of experience deploying ML solutions in production using cloud platforms (AWS, GCP, Azure) and Kubernetes.
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field (preferred).
3+ years of experience optimizing ML algorithms, infrastructure, and following software development best practices like CI/CD (preferred).
3+ years of experience with various ML techniques, architectures, and designing production-ready data pipelines (preferred).

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