AI Engineer 4 (MLX, Agentic AI, Gen AI workflows, GraphRAG)
Capital One3 days ago
Cambridge, MA, USA +4 moreStaff+
Base Salary
$215k - $246k/yr
Responsibilities
- Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
- Design, develop, test, deploy, and support foundation model training, LLM inference, agents, multi-agent workflows, similarity search, guardrails, evaluation, experimentation, governance, and observability components.
- Use AI technologies including AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch.
- Develop foundation model optimization techniques to improve scalability, cost, latency, throughput, and production performance.
- Own end-to-end architecture and contribute to the technical vision and roadmap for foundational AI systems.
- Define and maintain AI reliability SLOs covering latency, uptime, and model performance drift.
- Optimize GPU/TPU utilization and model inference pipelines with infrastructure engineering.
- Lead technical reviews for AI deployments and ensure security, data governance, compliance, and ethical standards.
- 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 a related field plus at least four years of experience developing AI and ML algorithms or technologies, or a master's degree in one of these fields plus at least two years of such experience.
- At least four years of programming experience with Python, Go, Scala, CUDA, or Java.
- Preferred experience leading AI system development with tradeoffs involving cost, latency, throughput, and accuracy.
- Preferred six or more years of deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
- Experience developing AI services, AI/ML algorithms, LLM inference, similarity search, VectorDBs, guardrails, memory, agentic AI systems, and agentic workflows.
- Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost.
- 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.
- Ability to influence architectural decisions across multiple AI product lines or platforms.
- Strong engineering, mathematics, hardware, software, and AI expertise, with the ability to apply emerging research in production.
Benefits
- Capital One offers health, financial, and other benefits supporting employee well-being, with eligibility varying by employment status and management level.
- The role is eligible for performance-based incentive compensation, including potential cash bonuses and/or long-term incentives.
Categories
About Capital One
Capital One is a U.S. consumer and commercial bank that offers credit cards, checking and savings accounts, auto financing, and lending to small and large businesses. It earns revenue from interest income and interchange/fees across its card and banking products, and develops cloud-based digital services. Founded in 1994, Capital One is publicly traded on the NYSE (COF) and is headquartered in McLean, Virginia.