Senior AI Platform Engineer – Gen AI & MLX

Capital One

San Jose (CA)

On-site

USD 215,000 - 246,000

Full time

11 days ago

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Job summary

Capital One seeks a Lead AI Engineer in San Jose, CA to design and deploy AI-powered services that transform customer experiences. You will collaborate with engineers, researchers, PMs and product managers to build scalable AI components across the foundation and inference stack.

The role emphasizes state-of-the-art ML, optimization of training and inference, and governance. A Bachelor's or Master's in CS/AI with several years of hands-on cloud AI deployment is required, with compensation

Qualifications

Bachelor’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.
At least 4 years of experience programming with Python, Go, Scala, or Java.
6 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
Experience designing, developing, delivering, and supporting AI services
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
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 abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production

Responsibilities

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, 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, Nemo Guardrails, PyTorch, and more.
Invent and introduce state-of-the-art LLM 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.