Machine Learning Engineer IV

Capital One Financial

Confirmed live today Low trust

Quick summary

Work type: On-site
Location: New York, NY
Salary: $215,200–$245,600 / yr
Posted: 2 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 $230k

$170k most similar roles pay here $269k

This role pays more than 64% of similar roles. Most pay $192,050–$241,750 — the shaded band above. At the midpoint, this role pays about $230k 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 joins a collaborative team to drive major AI transformations and scale production models. This role involves designing, building, and delivering machine learning components that solve real-world business problems while collaborating with Product and Data Science teams. Daily responsibilities include writing and testing application code, automating deployments, constructing optimized data pipelines, and monitoring models in production. The work focuses on ensuring responsible and explainable AI through robust governance and best practices. Candidates will utilize Python, Scala, or Java to develop solutions using frameworks like PyTorch and TensorFlow, along with libraries such as Pandas, NumPy, and Scikit-learn. Technical requirements include experience with Spark, Ray, Kubernetes, and cloud platforms like AWS, GCP, or Azure. The role addresses the challenge of scaling high-quality machine learning applications within a complex production environment.

What does a Machine Learning Engineer earn in New York?

Median $230775 from 44 postings across 15 companies.

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Skills

Python PyTorch TensorFlow Scikit-learn NumPy Pandas Spark Ray Kubernetes AWS GCP Azure Java Scala C++ CI/CD Data Pipelines Agile

What you'll do

Design, build, and deliver machine learning models and components to solve real-world business problems.
Make informed infrastructure decisions regarding model selection, data features, training, and hyperparameter tuning.
Write and test application code while automating tests and deployment processes for ML models.
Build and maintain optimized data pipelines to feed production-scale machine learning models.
Retrain, maintain, and monitor models in production environments to ensure consistent performance.
Develop cloud-based architectures and technologies to deliver optimized machine learning models at scale.
Implement CI/CD best practices and automated monitoring to ensure successful deployment of code and models.
Ensure all models follow best practices for Responsible and Explainable AI and meet risk governance standards.

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, and libraries like Pandas, NumPy, and Scikit-learn.
At least 4 years of experience operating large scale distributed systems such as 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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