Benefits and compensation include health, financial and other benefits that support total well-being, plus performance-based incentive compensation (cash bonuses and long-term incentives), discretionary or non-discretionary depending on the plan.
Location: McLean, VA (onsite)
Salary: USD 229,900 - 262,400 per year
Education: Bachelor's degree or Master's degree
Overview
Capital One is focused on building responsible and reliable AI systems and delivering real-time, personalized customer experiences. The company has a track record of leadership in applying machine learning to enhance banking, supported by strong technology infrastructure and top talent. This foundation positions the team to advance enterprise AI initiatives across the business.
Team
The Intelligent Foundations and Experiences (IFX) team sits at the center of Capital One's AI strategy. We collaborate across the organization to push the state of the art in AI engineering and deploy proprietary solutions that deliver value to millions of customers. Our AI models and platforms empower cross-functional teams to enhance products with the transformative power of AI.
In This Role
- Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI powered products that impact 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 performance metrics such as scalability, cost, latency, and throughput for large scale production AI systems.
- Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
The Ideal Candidate
- You enjoy building systems and take pride in the quality of your work while aligning with responsible AI goals.
- You stay current with the latest research and can translate scientific publications into production-worthy approaches.
- You adapt quickly, seek clarity in complex problems, ask questions, and communicate findings concisely. You share new ideas even when unproven.
- You bring a strong technical foundation in engineering and mathematics, and you see optimization opportunities across hardware, software, and AI.
- You are resilient and willing to chart new paths to achieve business goals in uncertain environments.
Basic Qualifications
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 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 4 years of experience developing AI and ML algorithms or technologies.
- At least 6 years of experience programming with Python, Go, Scala, or Java.
Preferred Qualifications
- 7 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, integrating, delivering, and supporting complex AI systems.
- Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders.
- 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.
- A passion for staying abreast of the latest AI research and AI systems, and applying novel techniques in production.
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers.
Technologies
- AWS Ultraclusters
- Huggingface
- VectorDBs
- Nemo Guardrails
- PyTorch
- Python
- Go
- Scala
- Java
