Lead AI Engineer
| Company | Capital One |
|---|---|
| Location | Cambridge, MA, USA, San Francisco, CA, USA, San Jose, CA, USA, McLean, VA, USA, New York, NY, USA |
| Salary | $193400 – $240800 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior, Expert or higher |
Requirements
- Bachelor’s degree in Computer Science, Engineering, or AI plus at least 4 years of experience developing AI and ML algorithms or technologies, or Master’s degree plus at least 2 years of experience developing AI and ML algorithms or technologies
- At least 4 years of experience programming with Python, Go, Scala, or Java
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.
Preferred Qualifications
- 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
- 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 abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production