Distinguished AI Engineer (remote Eligible)

Capital One Capital One · Banking · San Francisco, CA +3 · Remote

This role focuses on designing, developing, testing, deploying, and supporting AI software components, including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will leverage various AI technologies and invent optimization techniques to improve the performance of large-scale production AI systems, contributing to the technical vision and roadmap of foundational AI systems. The role emphasizes building and deploying AI-powered products and foundational AI systems in a scalable and responsible manner.

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

Skills

Required

  • Bachelor's degree in Computer Science, Engineering, or AI plus at least 8 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 6 years of experience developing AI and ML algorithms or technologies
  • At least 8 years of experience programming with Python, Go, Scala, or Java

Nice to have

  • 8 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 VP level
  • 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
  • Master's degree in Computer Science, Computer Engineering, or relevant technical field
  • Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
  • Passion for staying on top 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

What the JD emphasized

  • responsible and reliable AI systems
  • responsible and scalable ways
  • state-of-the-art LLM optimization techniques
  • scalability, cost, latency, throughput
  • foundational AI systems
  • complex AI systems
  • optimizing training and inference software to improve hardware utilization, latency, throughput, and cost

Other signals

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput
  • foundational AI systems