AI Engineer 5 – MLX

Capital One

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

Posted 9/29/2026full-timeUnited StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and optimizing AI systems, including foundation model training and multi-model orchestration. Proficient in leveraging cloud platforms and open-source AI technologies to deliver scalable and efficient AI solutions.

Highest-signal resume keywords

AI Software DevelopmentPython ProgrammingCloud Platform DeploymentAI System OptimizationModel Evaluation and Governance

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI AlgorithmsMachine LearningFoundation Model TrainingMulti-Agent WorkflowsModel CompressionCUDA ProgrammingScalable AI SolutionsHeterogeneous AI Systems IntegrationDynamic Inference StrategiesCost-Performance Governance

Soft Skills

Excellent CommunicationMentoringCross-Domain Learning

Tools & Technologies

AWS UltraclustersHugging FaceVector DatabasesPyTorch

Industry Keywords

AI Engineering StandardsEthical AI DeploymentGPU UtilizationModel ThroughputInference Cost Efficiency

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, vector databases, and PyTorch
Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput of production AI systems
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 design councils or design review boards to ensure technical consistency and compliance with AI engineering standards
Mentor Principal- and Manager-level AI engineers and foster cross-domain learning

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 one of these fields plus at least 4 years of experience
At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
Passion for staying current with AI research and ability to apply novel techniques in production
Strong foundation in engineering and mathematics
Experience leading AI system development with cost, latency, throughput, and accuracy tradeoffs
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 developing AI/ML algorithms or technologies using 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)
Excellent communication and presentation skills (preferred)
Experience architecting and integrating heterogeneous AI systems into 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 (preferred)
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 who require them