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 if posting has real specific content. We need to extract content. The posting includes: - Intro paragraph about Capital One AI etc. Could be "About the role" maybe. - Team Description: description of IFX team. - What You’ll Do: responsibilities. - Basic Qualifications: requirements. - Preferred Qualifications: maybe qualifications/skills. - Pay: salary ranges per location. - Benefits: mention of comprehensive benefits. - Schedule: not mentioned explicitly; maybe not include. - Also mention sponsorship, etc. Might be metadata. We need to avoid boilerplate: EEO, legal, accommodation, etc. We need to preserve all substantive info. We need to use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. Do NOT repeat job title as heading. Do NOT put Job ID/requisition numbers as headings. We need to output only HTML fragment. Let's structure:

  About the role

  ... intro + team description maybe combined.

  Responsibilities

  • ... list from "What You’ll Do".

  Requirements

  • ... Basic Qualifications bullet points.

  Qualifications

  • ... Preferred Qualifications bullet points.

  Skills

  Maybe combine some skills from preferred? But we already have Qualifications. Could omit Skills if no distinct content. The instruction: prefer few substantive sections over many thin ones. We could merge Preferred Qualifications into Qualifications or Skills. But we have both Basic and Preferred; we could treat Basic as Requirements, Preferred as Qualifications. Skills maybe not needed.

  Benefits

  ... description of benefits.

  Pay

  • ... salary ranges per location.
  Schedule: not present, so omit. We must not include placeholder text like "commensurate with experience". Not present. We need to ensure we don't include boilerplate like "Capital One will consider sponsoring...". That's substantive? It's about sponsorship; could be included maybe in About the role or a note. It's not boilerplate; it's specific. Could include in About the role paragraph. We need to remove duplicated lines, navigation, legal, EEO, scam-warning, etc. So remove the long EEO paragraph, accommodation, etc. Also remove "This role is expected to accept applications for a minimum of 5 business days. No agencies please." That's boilerplate/application instructions; remove. Also remove "Capital One is an equal opportunity employer..." etc. Also remove "Capital One promotes a drug-free workplace." etc. Also remove "Capital One will consider for employment qualified applicants with a criminal history..." etc. Also remove "If you have visited our website..." accommodation. Also remove "For technical support or questions..." etc. Also remove "Capital One does not provide, endorse nor guarantee..." etc. Also remove "Capital One Financial is made up..." etc. Now let's extract substantive content. Intro: "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: "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 (Responsibilities): List: - 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 Note some lines have formatting issues; we keep as is. Basic Qualifications (Requirements): - 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 (Qualifications/Skills): - 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) Pay: - 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 - Note: 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. Benefits: - 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. We could keep that as prose. Now produce HTML. We need to wrap each section with

  heading and then content. For lists, use

  • ...

  . For prose, use

  . We need to ensure we don't include extra whitespace? It's fine. Let's craft. About the role: combine intro + team description + sponsorship note? Sponsorship note: "Capital One will consider sponsoring a new qualified applicant for employment authorization for this position." That's substantive; we can include in About the role. Thus About the role paragraph(s) maybe two

  but we can combine into one

  with sentences. Let's do:

  About the role

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

  Responsibilities: list each bullet. Requirements: list two bullets. Qualifications: list many bullets. Benefits: one paragraph. Pay: list each location plus note about other locations and incentive compensation. We could combine incentive compensation as a bullet. Let's produce. Check for any lef