Senior Software Engineer

Pearson VUE

. Lead the design and evolution of Pearson's Kubernetes-based machine learning platform supporting large-scale model training and deployment .

Posted 9/15/2026full-timeRemote • Texas • United StatesSenior💰 $135,000 - $155,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing Kubernetes-based machine learning platforms, with a strong focus on distributed systems, cloud-native applications, and backend service development. Proficient in Python and experienced in deploying production-level AI infrastructure while mentoring engineering teams and establishing best practices.

Highest-signal resume keywords

Kubernetes DevelopmentPython DevelopmentCloud-Native Applications on AWSMachine Learning Platforms (MetaFlow, MLflow)REST-Based APIs and Microservices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed Systems DevelopmentSQL and NoSQL DatabasesCI/CD PipelinesAutomated TestingGPU ComputingPerformance OptimizationLarge Language Model DeploymentMachine Learning Frameworks (PyTorch, TensorFlow)Infrastructure as CodeWorkload Optimization

Soft Skills

Problem-SolvingCommunicationCollaboration

Tools & Technologies

MetaFlowAWSKubernetesGitCI/CD Tools

Industry Keywords

Machine LearningAI InfrastructureCloud Cost ManagementDeveloper Productivity ToolsScalable Systems

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsKubernetesNoSQLPythonPyTorchSQLTensorflowGo

About the role

Key responsibilities & impact

Lead the design and evolution of Pearson's Kubernetes-based machine learning platform supporting large-scale model training and deployment
Design, implement, and optimize distributed machine learning workflows using MetaFlow and other cloud-native technologies
Build platform capabilities for reproducible experimentation, automated model training, artifact management, and production deployment
Develop infrastructure supporting GPU-based workloads for traditional machine learning models, foundational models, and agentic pipelines
Design and implement backend services and APIs supporting machine learning lifecycle management
Evaluate and integrate open-source technologies to improve developer productivity, platform reliability, scalability, and operational efficiency
Collaborate with AI scientists to transition research prototypes into robust, scalable, production-quality systems
Improve platform observability, reliability, security, and cloud cost efficiency
Mentor engineers, contribute to technical strategy, and establish engineering best practices across the team

Requirements

What you’ll need

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent professional experience
Strong software engineering experience developing complex distributed systems
Expert-level Python development
Experience designing and building cloud-native applications on AWS
Experience developing applications using Kubernetes and container technologies
Experience designing REST-based APIs and microservice architectures
Experience working with SQL and NoSQL databases
Experience with CI/CD pipelines, Git-based development workflows, and automated testing
Strong problem-solving, communication, and collaboration skills
Preferred: Experience with machine learning platforms such as MetaFlow, MLflow, Kubeflow, or similar workflow orchestration systems
Preferred: Experience with production machine learning systems
Preferred: Experience with GPU computing and distributed model training
Preferred: Experience with large language model deployment or inference infrastructure
Preferred: Experience with PyTorch, TensorFlow, or similar machine learning frameworks
Preferred: Experience with Kubernetes operations, scheduling, and workload optimization
Preferred: Go development
Preferred: Infrastructure as Code technologies
Preferred: Performance optimization and cloud cost management
Preferred: Building internal developer platforms or engineering productivity tools
Experience building platforms used by machine learning engineers and data scientists
Experience deploying and operating production AI or LLM infrastructure
Experience fine-tuning, deploying, and managing foundation models and pipelines
Experience designing highly scalable cloud-native systems handling large datasets and compute-intensive workloads
Curiosity about emerging AI technologies and ability to evaluate them pragmatically
Passion for building tools that enable others to move faster

Benefits

Comp & perks

No bonus eligibility
Benefits information is provided via the linked benefits page