AI Engineer 4 (AI Foundations: World Model, RL, Causal Inference, Sequential Data)
Capital One3 days ago
Cambridge, MA, USA +3 moreStaff+
Base Salary
$197k - $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 AI software components for foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, experimentation, governance, and observability.
- Develop optimization techniques for large-scale production AI systems to improve scalability, cost, latency, throughput, and hardware utilization.
- Own end-to-end architecture for complex AI systems and contribute to the long-term roadmap for foundational AI platforms.
- 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 technical reviews for AI system deployments, ensuring security, data governance, compliance, maintainability, and ethical alignment.
- 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 relevant experience.
- At least four years of programming experience with Python, Go, Scala, CUDA, or Java.
- Preferred experience leading AI system development with tradeoff decisions 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 designing, developing, delivering, and supporting AI services and AI/ML technologies such as LLM inference, similarity search, VectorDBs, guardrails, and memory.
- Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost.
- Experience building agentic AI systems and workflows and 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 and apply current AI research in production.
Benefits
- Hybrid work arrangement requiring adherence to Capital One’s required on-site presence policy.
- Comprehensive health, financial, and other benefits supporting employee well-being, with eligibility varying by employment status and management level.
- Eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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.