Lead AI Engineer (AI Foundations, LLM Core and Agentic AI)

About the Role

At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with partners across the company to advance the state of the art in science and AI engineering, building and deploying proprietary solutions central to our business that deliver value to millions of customers.

Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI in responsible and scalable ways for high-impact results.

Responsibilities

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how associates work and how customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance—scalability, cost, latency, throughput—of large-scale production AI systems.
• Contribute to the technical vision and long-term roadmap of foundational AI systems at Capital One.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies.
• OR a Master’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 2 years of experience developing AI and ML algorithms or technologies.
• At least 4 years of experience programming with Python, Go, Scala, or Java.

Preferred Qualifications

• 6 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g., AWS, Google Cloud, Azure, or equivalent private cloud).
• Experience designing, developing, delivering, and supporting AI services.
• Experience developing AI and ML algorithms or technologies (e.g., LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang.
• Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
• Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production.

Skills

• Deep technical foundation in engineering and mathematics.
• Expertise in hardware, software, and AI to identify and exploit optimization opportunities.
• Ability to adapt quickly and thrive on bringing clarity to big, undefined problems.
• Strong problem-solving skills with a focus on root-cause analysis and concise articulation of findings.
• Courage to share new ideas, even when unproven.
• Resilience and trailblazing mindset to forge new paths and achieve business goals in uncertain environments.

Pay

The annual salary ranges for this role are as follows:

• Cambridge, MA: $197,300 - $225,100
• McLean, VA: $197,300 - $225,100
• New York, NY: $215,200 - $245,600
• San Francisco, CA: $215,200 - $245,600
• San Jose, CA: $215,200 - $245,600

Candidates hired to work in other locations will be subject to the pay range associated with that location. This role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI).

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.