Build and operate machine learning systems at scale with a team that emphasizes production readiness, responsible model governance, and practical engineering rigor. This onsite role in New York, NY focuses on designing, building, deploying, and monitoring ML models and components, with collaboration across Product and Data Science. You’ll also work with cloud and distributed systems to support production operations, CI/CD practices, and Responsible and Explainable AI.
What you’ll do
- Design, build, and deliver ML models and components that solve real business problems in partnership with Product and Data Science teams
- Inform ML infrastructure decisions using knowledge of modeling considerations such as model selection, data and feature selection, 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 on a cross-functional Agile team to create and improve software for state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful deployments
- Manage code to reduce vulnerabilities, support risk-aware governance for models, and follow Responsible and Explainable AI best practices
- Use Python, Scala, or Java in daily engineering work
What you’ll bring
- Minimum: 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, plus libraries such as Pandas, NumPy, and Scikit-learn
- At least 4 years operating large-scale distributed systems (such as Spark and Ray) to prepare AI/ML data
- At least 2 years deploying and operating ML solutions in production, including cloud operation experience with AWS, GCP, or Azure, and managing containerized ML systems with Kubernetes
- Minimum experience: 2 years
Technologies you may work with
- Python, Scala, Java, Golang, C++
- PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
- Spark, Ray
- AWS, GCP, Azure
- Kubernetes
Incentives and benefits
- Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI); incentives may be discretionary or non-discretionary depending on the plan
- Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being
Preferred qualifications
- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
- 3+ years optimizing ML algorithms, configurations, and infrastructure
- 3+ years following software development best practices including source control, testing, code reviews, and CI/CD
- 3+ years building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
- 3+ years working with ML techniques (supervised, semi-supervised, unsupervised, reinforcement learning), including model types (regression, classification, clustering), model architectures (RNNs, CNNs, LSTMs, Transformers), and evaluating and diagnosing common issues such as underfitting and overfitting
- 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
- 1+ years as a technical lead developing ML solutions using best practices, patterns, and automation
- Authored or co-authored a paper on an ML technique, model, or proof of concept
Salary, location, and employment details
- Location: New York, NY (onsite)
- Salary: USD 215,200 - 245,600 per year (Machine Learning Engineer 4). McLean, VA range listed as USD 197,300 - 225,100.
- Employment type: Full-time
- This role is expected to accept applications for a minimum of 5 business days. No agencies please.
Work authorization
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (including H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer).
