AI Engineer 5 – AI Foundations

Capital One

. Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products .

Posted 9/18/2026full-timeSan Jose • California • United StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI software components, including foundation model training and LLM inference, while leading technical governance and mentoring teams. Proficient in leveraging cloud platforms and open-source AI technologies to deliver scalable and responsible AI solutions.

Highest-signal resume keywords

AI Software DevelopmentPython ProgrammingLLM InferenceCost-Performance GovernanceAI System Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AlgorithmsMachine LearningFoundation Model TrainingMulti-Agent WorkflowsCUDA ProgrammingJava ProgrammingGo ProgrammingScala ProgrammingModel EvaluationOptimization Techniques

Soft Skills

Excellent CommunicationPresentation SkillsMentoring

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorch

Industry Keywords

AI Engineering StandardsEthical AI DeploymentScalable AI SolutionsDynamic Inference StrategiesModel Compression

Tech Stack

Tools & technologies

AWSCloudJavaPythonPyTorchScalaC++Go

About the role

Key responsibilities & impact

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
Leverage open-source and SaaS AI technologies such as AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch
Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput
Contribute to the technical vision and long-term roadmap of foundational AI systems
Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency
Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards
Mentor Principal- and Manager-level AI engineers and elevate organizational technical maturity

Requirements

What you’ll need

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, OR a Master's degree in those fields plus at least 4 years of experience developing AI and ML algorithms or technologies
At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
Strong foundation in engineering and mathematics
Expertise in hardware, software, and AI
Ability to understand scientific publications and apply novel techniques in production
Preferred: experience leading AI system development with cost, latency, throughput, and accuracy tradeoffs
Preferred: 7 years of experience deploying scalable and responsible AI solutions on cloud platforms
Preferred: experience designing, developing, delivering, and supporting complex AI systems
Preferred: experience with LLM inference, similarity search, VectorDBs, guardrails, memory, and programming languages including Python, C++, C#, Java, CUDA, or Golang
Preferred: experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
Preferred: experience building agentic AI systems and workflows
Preferred: experience architecting heterogeneous AI systems and unified production pipelines
Preferred: experience defining and enforcing ethical AI deployment standards
Preferred: ability to balance model performance and operational cost through dynamic inference strategies and model compression
Preferred: experience right-sizing models, instance counts, and hardware types
Excellent communication and presentation skills
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial and other benefits supporting total well-being
Employment authorization sponsorship may be considered for a new qualified applicant
Reasonable accommodations for applicants with disabilities