# Machine Learning Engineer 4 - Manager IC

**Company**: Capital One
**Location**: New York, NY
**Experience**: senior
**Job type**: full-time
**Salary**: $197,300 - $225,100 (McLean, VA), $215,200 - $245,600 (New York, NY and San Jose, CA)
**Category**: Engineering
**Industry**: Finance

**Apply**: https://capitalone.wd12.myworkdayjobs.com/en-US/Capital_One/job/New-York-NY/Machine-Learning-Engineer-4---Manager-IC_R1005241-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_32b8ffec-543

## Description

We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team.

As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.

As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One,ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask.

**What You’ll Do:**

- Design, build, and deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams

- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation

- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment

- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications

- Retrain, maintain, and monitor models in production

- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale

- Construct optimized data pipelines to feed ML models

- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code

- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI

- Use programming languages like Python, Scala, or Java

**Basic Qualifications:**

- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)

- At least 4 years of experience programming with Python, Java, Golang, or C++

- At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)

- At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data

- At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems

**Preferred Qualifications:**

- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field

- 3+ years designing, training, optimizing, and evaluating modern ML models across architectures (e.g., Transformers, CNNs, RNNs) and paradigms (supervised, unsupervised, reinforcement learning), including loss design, hyperparameter tuning, and diagnostic debugging

- 3+ years architecting and scaling distributed, end-to-end ML systems, from production-ready training/evaluation data pipelines to algorithm and infrastructure optimization

- 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools (e.g., Kubeflow is a strong plus)

- 3+ years applying modern software best practices (CI/CD, version control, automated testing, code reviews) and deploying resilient systems with progressive rollout strategies (blue/green, canary/one-box), monitoring, alerting, and incident response planning

- 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation

- Authored/co-authored a paper on a ML technique, model, or proof of concept

**Salary Information:**

- McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4

- New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4

- San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4

**Benefits:**

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.

## Skills

### Required
- Python
- Java
- Golang
- C++
- PyTorch
- Tensorflow
- Pandas
- NumPy
- Scikit-learn
- Spark
- Ray
- Kubernetes
- Docker
- Kubeflow

### Nice to have
- Transformers
- CNNs
- RNNs
- loss design
- hyperparameter tuning
- diagnostic debugging
- distributed systems
- CI/CD
- version control
- automated testing
- code reviews

---

Source: [Apply at capitalone.wd12.myworkdayjobs.com](https://capitalone.wd12.myworkdayjobs.com/en-US/Capital_One/job/New-York-NY/Machine-Learning-Engineer-4---Manager-IC_R1005241-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)