AI Engineer 4 (AI Foundations: Benchmarking, Evaluation, and Explainability)
$197k - $225k • McLean, VA • Full-time
Posted 1mo ago
About the job
Capital One is a leader in using machine learning to create personalized customer experiences and is committed to building world-class applied science and engineering teams to deliver breakthrough product experiences and scalable, high-performance AI infrastructure. The Intelligent Foundations and Experiences (IFX) team is at the center of bringing this vision to life, working with partners across the company to advance AI science and engineering and build proprietary solutions. Our AI models and platforms empower teams across Capital One to enhance their products with AI in responsible and scalable ways, delivering value to millions of customers.
Responsibilities
- Partner with cross-functional teams to deliver AI-powered products.
- Design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, agents, similarity search, guardrails, model evaluation, and observability.
- Leverage Open Source and SaaS AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
- Invent and introduce state-of-the-art foundation model optimization techniques to improve performance of large-scale production AI systems.
- Contribute to the technical vision and roadmap of foundational AI systems.
- Own the end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
- Define and maintain service-level objectives (SLOs) for AI reliability.
- Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines.
- Lead technical reviews for new AI system deployments, ensuring security, data governance, and compliance.
- Mentor 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 a Master's degree plus at least 2 years of experience.
- At least 4 years of experience programming with Python, Go, Scala, CUDA, or Java.
- Experience leading development of AI systems with tradeoff decisions around cost, latency, throughput, and accuracy.
- 6+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure).
- 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, CUDA, or Golang.
- Experience developing and applying state-of-the-art techniques for optimizing training and inference software.
- Experience in building agentic AI systems and agentic workflows.
- Passion for staying abreast of the latest AI research and applying novel techniques in production.
- Proficiency in designing distributed systems for model training, evaluation, and online inference.
- Experience defining AI model governance processes.
- Demonstrated ability to influence architectural decisions across multiple teams.