
1001 - 5000 employees
Founded 1994
💼 Consulting
🏥 Healthcare
📦 Logistics
Consulting • Healthcare • Logistics
Pearson VUE is a global leader in computer-based testing, providing a wide range of credentialing and certification exams for various industries. They support test-takers and test owners by offering resources, scheduling options, and accommodations to ensure equitable access to testing. Their mission is to empower candidates and enrich communities through the delivery of high-stakes exams that validate professional skills and knowledge, contributing to career advancement and industry standards.
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1001 - 5000 employees
Founded 1994
💼 Consulting
🏥 Healthcare
📦 Logistics
Consulting • Healthcare • Logistics
Pearson VUE is a global leader in computer-based testing, providing a wide range of credentialing and certification exams for various industries. They support test-takers and test owners by offering resources, scheduling options, and accommodations to ensure equitable access to testing. Their mission is to empower candidates and enrich communities through the delivery of high-stakes exams that validate professional skills and knowledge, contributing to career advancement and industry standards.
• 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
• 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
• No bonus eligibility • Benefits information is provided via the linked benefits page
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