Sr. Distinguished AI Engineer (remote Eligible)

Capital One Capital One · Banking · San Francisco, CA +4 · Remote

Sr. Distinguished AI Engineer at Capital One focused on building and deploying responsible and reliable AI systems, including foundation model training, LLM inference, similarity search, guardrails, and model evaluation. The role involves optimizing LLM performance for scalability, cost, and latency, and contributing to the technical vision for foundational AI systems. Requires strong engineering and AI expertise, experience with cloud platforms, and the ability to lead and mentor.

What you'd actually do

  1. 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.
  2. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  3. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
  4. 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.
  5. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.

Skills

Required

  • Python
  • Go
  • Scala
  • Java
  • C++
  • C#
  • Golang
  • Computer Science
  • AI
  • Electrical Engineering
  • Computer Engineering
  • developing AI and ML algorithms or technologies
  • programming
  • developing and applying state-of-the-art techniques for optimizing training and inference software

Nice to have

  • AWS
  • Google Cloud
  • Azure
  • private cloud
  • architecting, designing, developing, integrating, delivering, and supporting complex enterprise AI systems
  • lead and mentor an engineering organization
  • influence cross-functional stakeholders
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • improve hardware utilization, latency, throughput, and cost
  • staying abreast of the latest AI research and AI systems
  • judiciously apply novel techniques in production
  • Excellent communication and presentation skills
  • articulate complex AI concepts to peers
  • building and deploying multi-modal models
  • computer vision
  • speech recognition
  • optical character recognition
  • robotics
  • digital assistants
  • industrial automation
  • autonomous driving

What the JD emphasized

  • Sr. Distinguished AI Engineer
  • AI software components
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability, cost, latency, throughput
  • large scale production AI systems
  • foundational AI systems
  • AI-powered products
  • AWS Ultraclusters
  • Huggingface
  • VectorDBs
  • Nemo Guardrails
  • PyTorch
  • AI and ML algorithms or technologies
  • deploying scalable and responsible AI solutions
  • complex enterprise AI systems
  • lead and mentor an engineering organization
  • influence cross-functional stakeholders
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • optimizing training and inference software
  • hardware utilization, latency, throughput, and cost
  • AI research and AI systems
  • novel techniques in production
  • complex AI concepts
  • multi-modal models
  • computer vision
  • speech recognition
  • optical character recognition
  • robotics
  • digital assistants
  • industrial automation
  • autonomous driving

Other signals

  • building world-class applied science and engineering teams
  • deliver our industry leading capabilities with breakthrough product experiences
  • scalable, high-performance AI infrastructure
  • transformative power of emerging AI capabilities
  • advance the state of the art in science and AI engineering
  • build and deploy proprietary solutions that are central to our business
  • AI models and platforms empower teams across Capital One
  • responsible and scalable ways for the highest leverage impact
  • deliver AI-powered products
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • AWS Ultraclusters
  • Huggingface
  • VectorDBs
  • Nemo Guardrails
  • PyTorch
  • state-of-the-art LLM optimization techniques
  • performance — scalability, cost, latency, throughput
  • large scale production AI systems
  • technical vision and the long term roadmap of foundational AI systems
  • build systems
  • quality of your work
  • do the right thing
  • problems that will help change banking for good
  • staying abreast of the latest research
  • ability to intuitively understand scientific publications
  • judiciously apply novel techniques in production
  • adapt quickly
  • thrive on bringing clarity to big, undefined problems
  • asking questions
  • digging deep to uncover the root of problems
  • articulate your findings concisely with clarity
  • courage to share new ideas even when they are unproven
  • deeply Technical
  • strong foundation in engineering and mathematics
  • expertise in hardware, software, and AI
  • see and exploit optimization opportunities that others miss
  • resilient trail blazer
  • forge new paths to achieve business goals when the route is unknown
  • developing AI and ML algorithms or technologies
  • deploying scalable and responsible AI solutions on cloud platforms
  • architecting, designing, developing, integrating, delivering, and supporting complex enterprise AI systems
  • lead and mentor an engineering organization
  • influence cross-functional stakeholders
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • optimizing training and inference software
  • improve hardware utilization, latency, throughput, and cost
  • staying abreast of the latest AI research and AI systems
  • judiciously apply novel techniques in production
  • Excellent communication and presentation skills
  • articulate complex AI concepts to peers
  • building and deploying multi-modal models
  • computer vision
  • speech recognition
  • optical character recognition
  • robotics
  • digital assistants
  • industrial automation
  • autonomous driving