Senior Manager, Machine Learning Engineer (People Leader)

The Senior Manager, Machine Learning Engineer (People Leader) role at Capital One is focused on leading ML engineering initiatives while contributing hands-on to the design, build, deployment, monitoring, and governance of production machine learning systems at scale. The position also emphasizes mentoring engineers and building durable teams through recruiting and talent development.

Location

Plano, TX (onsite)

Compensation

USD 209,000 - 238,500 per year

Role Overview

In this role, you will collaborate with Product and Data Science teams to deliver ML solutions to real-world business needs. You will help scale multi-tenant platforms for training and serving large-footprint ML models, guide ML infrastructure choices, and ensure production systems are retrained, maintained, and monitored. As a people leader, you will mentor peers and junior engineers and support recruiting, nurturing, and retention of top engineering talent.

Responsibilities

• Design, build, and/or deliver ML models and components to address business problems in collaboration with Product and Data Science teams
• Build and scale large multi-tenant platforms for running large-footprint ML model training and/or serving
• Influence ML infrastructure decisions using expertise in model choice, data and feature selection, training methods, hyperparameter tuning, dimensionality, bias/variance, and validation
• Solve complex problems through application code development and testing, building and validating ML models, and automating tests and deployment
• Collaborate within cross-functional Agile teams to create and improve software for big data and ML applications
• Retrain, maintain, and monitor models in production
• Use or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to supply ML models
• Apply CI/CD best practices, including test automation and monitoring, to support successful model and application deployments
• Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML practices align with Responsible and Explainable AI
• Act as a force-multiplier by balancing deep technical contributions with mentoring and skill elevation for peers and junior engineers
• Recruit, nurture, and retain top engineering talent
• Stay current with technology trends through experimentation and learning, participate in internal and external technology communities, and mentor others

Minimum 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 6 years of experience programming with Python, Java, Golang, or C++
• At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
• At least 4 years of experience deploying and operating Machine Learning solutions in production, operating production services in the cloud (AWS, GCP, Azure), and using Kubernetes for large scale containerized ML systems
• At least 1 year of people leadership experience

Technologies

Python, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn, Spark, Ray, AWS, GCP, Azure, Kubernetes, CI/CD, Responsible and Explainable AI

Benefits

• Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial and other benefits that support total well-being

Preferred Qualifications

• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
• 3+ years of experience managing and leading an ML engineering team
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
• 5+ years of experience 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
• 5+ years of experience working with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy and diagnosing common issues (underfitting, overfitting)
• 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

Additional Salary Information by Location

• McLean, VA: $229,900 - $262,400
• New York, NY: $250,800 - $286,200
• Plano, TX: $209,000 - $238,500
• San Francisco, CA: $250,800 - $286,200

Additional Information

• Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
• This role is expected to accept applications for a minimum of 5 business days.
• No agencies please.
• Capital One is an equal opportunity employer (EOE), including disability/veteran, committed to non-discrimination in compliance with applicable federal, state, and local laws.
• Capital One promotes a drug-free workplace.
• Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries.
• If you require an accommodation for the application process, contact Capital One Recruiting at 1-800-304-9102 or [email protected].
• For technical support or questions about Capital One's recruiting process, send an email to [email protected].
• Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site and is not liable for them.
• Capital One Financial is made up of several different entities (Canada, United Kingdom, Philippines).