AI Engineer 4

Capital One’s IFX team is hiring an AI Engineer 4 to design, build, and operate responsible, reliable AI software systems.

Responsibilities

• Collaborate with cross-functional teams including engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers
• Design, develop, test, deploy, and support AI software components such as foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
• Use a broad mix of Open Source and SaaS AI tooling, including AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and related technologies
• Develop state-of-the-art foundation model optimization approaches to improve scalability, cost, latency, and throughput for production AI systems
• Help shape the technical vision and long-term roadmap for foundational AI systems at Capital One
• Own end-to-end architecture for complex AI systems, focusing on maintainability, observability, and ethical alignment
• Define and maintain service-level objectives (SLOs) for AI reliability, including latency, uptime, and model performance drift
• Partner with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines
• Lead cross-functional technical reviews for new AI system deployments, ensuring security, data governance, and compliance standards are met
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation

Requirements

• 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; or Master’s degree in a related field plus at least 2 years of experience developing AI and ML algorithms or technologies
• At least 4 years programming with Python, Go, Scala, CUDA, or Java

Technology Areas

• AWS Ultraclusters, Huggingface, VectorDBs, PyTorch
• AWS, Google Cloud, Azure
• Python, Go, Scala, CUDA, Java, C++, C#, Golang
• TPU, LLM Inference, Similarity Search, Guardrails, Memory

Benefits

• Comprehensive, competitive, and inclusive set of health, financial, and other benefits to support total well-being

Additional Details

• Location: McLean, VA (hybrid)
• Hybrid policy: expected to follow Capital One’s required on-site presence at the designated office location
• Work authorization: Capital One may consider sponsoring a new qualified applicant for employment authorization
• Incentives: eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI)
• Application window: expected to accept applications for a minimum of 5 business days
• Agencies: no agencies please

Salary

• USD 197,300 to 225,100 per year

Preferred Qualifications

• Experience leading development of AI systems with tradeoffs across cost, latency, throughput, and accuracy
• 6+ years 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/ML algorithms or technologies (e.g., LLM Inference, Similarity Search, VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang
• Experience applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
• Experience building agentic AI systems and agentic 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 retention schedules
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms
• Passion for staying current with AI research and applying novel techniques in production