AI Engineer 4 (AI Safety and Multimodal)
About The Position
Capital One is hiring an AI Engineer 4 for the Intelligent Foundations and Experiences (IFX) team, which develops and deploys proprietary AI solutions and platforms for use across the company. The role partners with engineers, research scientists, technical program managers, and product managers to build AI-powered products and foundational systems that improve how associates work and how customers interact with Capital One. The engineer designs, develops, tests, deploys, and supports AI software components, including foundation-model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role also develops optimization techniques for production AI systems, contributing to their scalability, cost efficiency, latency, and throughput. It owns architecture for complex AI systems and helps set the technical vision and long-term roadmap for foundational AI at Capital One. This position emphasizes AI safety and multimodal systems, including red teaming and deployment of safety guardrails for vision-language and multimodal language models, as well as multimodal retrieval-augmented generation architectures. The engineer works with infrastructure teams on GPU/TPU utilization and inference pipelines, establishes reliability objectives, leads deployment reviews, and mentors colleagues. The role is eligible for performance-based incentive compensation. Capital One will consider sponsoring a new qualified applicant for employment authorization.
Requirements
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 4 years of experience developing AI/ML algorithms or technologies; or a relevant master's degree and at least 2 years of such experience.
- At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
- Experience in AI red teaming and deploying safety guardrails for multimodal systems, including VLMs, MLLMs, and MM-RAG architectures.
Nice To Haves
- Experience leading AI-system development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
- 6 or more years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
- Experience designing, developing, delivering, and supporting AI services.
- Experience developing AI/ML algorithms or technologies such as LLM inference, similarity search, vector databases, guardrails, and memory, using Python, C++, C#, Java, CUDA, or Golang.
- Experience developing and applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
- Experience in AI safety and LLM safety alignment.
- Experience assessing the safety and vulnerabilities of multimodal applications using image/video and text modalities.
- Experience building agentic AI systems and workflows.
- Interest in keeping abreast of AI research and systems and applying novel techniques judiciously in production.
- Proficiency designing distributed systems for model training, evaluation, and online inference at petabyte scale.
- Experience defining AI model governance processes, including producibility, lineage tracking, and automated retraining schedules.
- Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.
Responsibilities
- Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
- Design, develop, test, deploy, and support AI software components, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
- Use open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch.
- Develop foundation-model optimization techniques to improve production AI system scalability, cost, latency, and throughput.
- Contribute to the technical vision and long-term roadmap of foundational AI systems at Capital One.
- Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
- Define and maintain service-level objectives for AI reliability, including latency, uptime, and model-performance drift.
- Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines.
- Lead cross-functional technical reviews for AI deployments, ensuring security, data governance, and compliance standards are met.
- Mentor Principal and Senior Associates in scalable design, performance tuning, and translating research into production.
- Apply AI red teaming and deploy safety guardrails for multimodal systems, including VLMs, MLLMs, and MM-RAG architectures.
Benefits
- Comprehensive health, financial, and other benefits supporting total well-being.
- Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
