Sr. Staff AI Engineer
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.
- Define and steer the technical AI architecture vision, integrating applied research breakthroughs into production ecosystems with reliability and scale
- Lead the establishment of AI performance, safety, and transparency standards that guide all model development and deployment company-wide
- Drive multi-year platform initiatives that unify data, compute and model lifecycle management under and cohesive enterprise AI architecture
- Mentor senior technical leaders across research, data and engineering disciplines, developing the next generation of Capital One’s AI technical leadership
Basic Qualifications:
- Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 10 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 8 years of experience developing AI and ML algorithms or technologies
- At least 10 years of experience programming with Python, Go, Scala, CUDA, or Java
Preferred Qualifications:
- Experience architecting AI platforms with tradeoff decisions around cost, latency, throughput and accuracy
- 9 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 SVP 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
- Recognized industry leader in applied AI or machine learning infrastructure through patents, publications, or open-source leadership
- Demonstrated experience designing long-term AI infrastructure strategies - balancing cost, scale, ethics and regulatory compliance
- Experience driving organization-wide adoption of AI safety, alignment and governance standards, collaborating with policy, risk and legal teams
- Proven ability to shape R&D investment strategy by identifying breakthrough AI capabilities with material business impact
- 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.
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
Cambridge, MA: $314,800 - $359,300 for Sr. Staff AI EngineerMcLean, VA: $314,800 - $359,300 for Sr. Staff AI Engineer
New York, NY: $343,400 - $392,000 for Sr. Staff AI Engineer
Richmond, VA: $286,200 - $326,700 for Sr. Staff AI Engineer
San Francisco, CA: $343,400 - $392,000 for Sr. Staff AI Engineer
San Jose, CA: $343,400 - $392,000 for Sr. Staff AI Engineer
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.