AI Engineer 4 (AI Safety and Multimodal)

Capital One • San Jose, CA

2d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms that enable teams across the company to deliver responsible, scalable AI products. The AI Engineer 4 partners with engineers, research scientists, technical program managers, and product managers to develop AI-powered products for associates and customers.

The role 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. It uses open-source and SaaS technologies and develops optimization techniques to improve the scalability, cost, latency, and throughput of production AI systems. The position also contributes to the technical vision and roadmap for foundational AI systems at Capital One.

The engineer owns architecture for complex AI systems, maintains AI reliability objectives, works with infrastructure engineering to optimize GPU/TPU utilization and inference pipelines, and leads technical reviews for deployments. The role includes mentoring Principal and Senior Associates. The ideal candidate is deeply technical, interested in applying current research to production, comfortable with ambiguous problems, and experienced with AI red teaming and safety guardrails for multimodal systems.

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 and ML algorithms or technologies; or a master’s degree in one of these fields and at least 2 years of relevant experience.
• At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
• Experience with AI red teaming and deploying safety guardrails for multimodal systems, including VLMs, MLLMs, and MM-RAG architectures.
• Strong engineering and mathematics foundation and expertise in hardware, software, and AI.
• Ability to understand scientific publications and apply novel techniques in production.
• Ability to adapt to ambiguous problems, investigate root causes, and communicate findings clearly.

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 and ML algorithms or technologies, such as LLM inference, similarity search, VectorDBs, 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 safety and vulnerabilities in multimodal applications using image/video plus text modalities.
• Experience building agentic AI systems and workflows.
• 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.
• Passion for keeping current with AI research and systems and applying new techniques in production.

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, such as AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch.
• Develop and introduce 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 the 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 system 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior