Sr. Lead AI Engineer (Inference Optimization, FM hosting, AI Platform)

hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. </span></p></li></ul><p></p><ul><li><p>You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. </p></li></ul><p></p><p>Basic Qualifications:</p><p></p><ul><li><p>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</p></li><li><p>At least 6 years of experience programming with Python, Go, Scala, or Java</p></li></ul><p></p><p>Preferred Qualifications:</p><ul><li><p>7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)</p></li><li><p>Experience designing, developing, integrating, delivering, and supporting complex AI systems </p></li><li><p>Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders</p></li><li><p>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</p></li><li><p>Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost</p></li><li><p>Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production</p></li><li><p>Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers</p></li></ul><p>
</p><p>Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.</p><p style="text-align:inherit"></p><p style="text-align:left">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.</p><p style="text-align:inherit"></p>Cambridge, MA: $229,900 - $262,400 for Sr. Lead AI Engineer<p>
</p>McLean, VA: $229,900 - $262,400 for Sr. Lead AI Engineer<p>
</p>New York, NY: $250,800 - $286,200 for Sr. Lead AI Engineer<p>
</p>San Francisco, CA

$250,800 - $286,200 for Sr. Lead AI Engineer<p>
</p>San Jose, CA: $250,800 - $286,200 for Sr. Lead AI Engineer<p>
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</p><p style="text-align:inherit"></p><p style="text-align:left">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.</p><p style="text-align:inherit"></p>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.<p style="text-align:inherit"></p><p style="text-align:left">Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your

Sr. Lead AI Engineer (Inference Optimization, FM hosting, AI Platform)<p style="text-align:inherit"></p><p style="text-align:inherit"></p><p>Overview:</p><p></p><p>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.</p><p></p><p>Team Description:</p><p></p><p>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.
</p><p>In this role, you will: </p><p></p><ul><li><p>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.</p></li></ul><p></p><ul><li><p>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, etc.</p></li></ul><p></p><ul><li><p>Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.</p></li></ul><p></p><ul><li><p>Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems.</p></li></ul><p></p><ul><li><p>Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital