Staff AI Engineer

Staff AI Engineer

At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. 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.

Team Description:

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 our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and 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 the highest leverage impact.

What You'll Do:

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
• Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
• Set the technical direction for enterprise-wide AI architecture - unifying tooling, observability, and deployment standards across teams
• Own the design and integration of model routing, caching, and orchestration systems that support hybrid and multi-model workloads
• Champion responsible AI principles across all system design, embedding transparency, reproducibility, and fairness-by-design
• Drive internal education, mentorship, and dissemination of best practices through architecture councils and AI guilds

The Ideal Candidate:

• You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good
• Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production
• You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven
• You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss
• You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown

Basic Qualifications:

• Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 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 6 years of experience developing AI and ML algorithms or technologies
• At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java

Preferred Qualifications:

• Experience designing AI systems with tradeoff decisions around cost, latency, throughput and accuracy
• 8 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
• Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems
• Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level
• Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, 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
• Experience in building agentic AI systems and agentic workflows
• Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
• Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
• Proven track record of defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability and evaluation frameworks
• Experience leading federated or multi-cloud AI strategies to maximize resilience, compliance and compute efficiency
• Demonstrated success influencing research-to-production promotion processes - guiding model handoff, evaluation and productization
• Experience defining north-star metrics for AI systems that balance business value, innovation velocity, cost, and ethical responsibility
• Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs)

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.