Sr. Staff AI Engineer

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info, not invent or summarize away detail. Remove boilerplate, navigation, legal, EEO, scam warning, duplicated lines, application instructions. We need to parse the posting. First paragraph: "At Capital One, we are creating responsible and reliable AI systems, changing banking for good. ..." This is about the role? It's more about company and team. Could be "About the role" or maybe "Team Description". But we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content accordingly. We have "Team Description: The Intelligent Foundations and Experiences (IFX) team ..." That's about the role/team. Could go under About the role. Then "What You’ll Do:" list of duties. That's Responsibilities. Then "Basic Qualifications:" list. That's Requirements? Actually Basic Qualifications are minimum requirements. Could be Requirements. Then "Preferred Qualifications:" list. That's Qualifications? Or Skills? The headings we have: Requirements, Qualifications, Skills. We need to decide mapping. Usually Requirements = basic qualifications. Qualifications = preferred qualifications. Skills = maybe specific technical skills? But we have many skill-like items in preferred qualifications. Could put them under Skills. However we must not invent sections if content is thin. We have content for both basic and preferred qualifications. So we can have Requirements (basic) and Qualifications (preferred). Skills maybe we could extract specific skills like programming languages, tools, etc. But we might just put them under Qualifications as well. However we have separate headings allowed; we could use Skills for the list of technical skills (Python, Go, etc.) but that would duplicate content. Better to follow typical pattern: Requirements = basic qualifications, Qualifications = preferred qualifications, Skills maybe not needed if not distinct. But we must only include sections when posting has real specific content. If we don't have distinct skills beyond those already in qualifications, we could omit Skills section. Similarly Benefits: posting mentions "Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being." That's a benefits statement but vague. It says "Learn more at the Capital One Careers website." Not specific. The rule: never emit a heading followed by filler/placeholder text (e.g., "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. Benefits: they mention benefits but not specific details. Is that considered specific enough? It says "comprehensive, competitive, and inclusive set of health, financial and other benefits". That's vague but still a statement about benefits. Might be considered filler? The instruction: "never create Pay/Schedule/Benefits sections unless the posting actually states them." It does state that they offer benefits, but not specifics. I'd argue it's still stating benefits exist, but not detailed. However the rule about filler/placeholder text: they gave examples like "commensurate with experience", "not specified", "N/A", "varies", "TBD". The benefits statement is not exactly those, but it's still vague. Might be considered filler. Safer to omit Benefits section unless we have concrete benefits like health insurance, 401k, etc. Not present. So omit Benefits. Pay: they give salary ranges per location. That's specific. So we must include Pay section. Schedule: not mentioned. So omit Schedule. Now we need to output HTML with headings

for each section. Use

• for any list of duties/requirements/benefits, and

  for prose. We need to preserve all substantive information; do not invent or summarize away detail. So we need to include all details from Basic Qualifications, Preferred Qualifications, What You’ll Do, Team Description, etc. Also the introductory paragraphs about company mission etc. Should we include them? They are substantive. They could go under About the role. We must not repeat job title. Not include Job ID etc. We must remove boilerplate: EEO, accommodation, etc. Also remove lines like "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." That's substantive? It's about sponsorship. Might be considered relevant. It's not boilerplate? It's about immigration sponsorship. Could be kept under About the role or maybe Requirements? It's a note about eligibility. It's substantive. We'll keep it. Also remove lines like "This role is also eligible to earn performance based incentive compensation..." That's about compensation; could go under Pay? Actually Pay section already includes salary ranges; incentive compensation is extra. Could be included in Pay section as a note. Also remove lines about "Learn more at the Capital One Careers website." That's generic; maybe omit. Also remove "Eligibility varies based on full or part-time status..." that's benefits eligibility; but we omitted Benefits. Also remove "This role is expected to accept applications for a minimum of 5 business days. No agencies please." That's application instruction; remove. Also remove EEO statement, drug-free workplace, criminal history, accommodation contact, technical support email, third-party disclaimer, etc. Now we need to structure. Let's extract sections: About the role: combine introductory paragraphs about Capital One AI vision, team description, maybe sponsorship note. Responsibilities: the "What You’ll Do:" bullet list (actually it's a paragraph with multiple sentences separated by periods). We need to turn into list items. The text: "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" We need to split into sensible list items. Each sentence seems a separate duty. We'll make

• for each. Requirements (Basic Qualifications): The text: "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" Note there is a missing period; but we need to preserve. We'll make list items: maybe two items: education+experience, and programming experience. Could also split the "or" into two alternatives but we can keep as one item. Qualifications (Preferred Qualifications): The list is long. We'll break each sentence or clause into list items. Let's extract Preferred Qualifications text: "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)" We'll make each bullet a
• . Pay: we need to include salary ranges per location. The text: "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 Engineer McLean, 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." We need to preserve all substantive info. We'll make a

  for the introductory sentences, then a

  with each location as
  • . Also include note about other locations and incentive compensation. We must not include fluff like "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." That's substantive? It's a clarification; we can keep. Now About the role: we need to include the introductory paragraphs about Capital One AI vision, team description, sponsorship note. Let's extract: "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