Advanced Specialist, AI Scientist

Pearson VUE

AI Scientist delivering scalable machine learning solutions for Pearson's educational products. Collaborating with business units to understand AI-related problems and deliver effective solutions.

Posted 7/14/2026full-timeRemote • 🇵🇱 PolandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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

Demonstrates strong applied experience in machine learning and deep learning, with proficiency in Python and core ML frameworks. Capable of mentoring peers, shaping project direction, and delivering robust AI solutions that meet stakeholder needs.

Highest-signal resume keywords

Machine LearningDeep LearningPython ProgrammingPyTorchTensorFlow

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningModel EvaluationExperimentationIteration at Scale

Soft Skills

Stakeholder EngagementMentoringTechnical Decision-MakingFeedback Provision

Tools & Technologies

PyTorchTensorFlowHugging FaceScikit-learn

Industry Keywords

AI SolutionsTechnical DocumentationUse Case DiscoveryTechnical Solutioning

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Deliver robust, scalable ML solutions for AI capabilities in educational products, taking significant implementation decisions independently.
Work closely with Business Unit partners to understand their AI-related problems, help frame them clearly, and develop solutions that fit their actual needs.
Identify opportunities to reuse existing AI components and capabilities across teams. This includes use case discovery, technical solutioning, and making a credible case to partner teams for why reuse is the right path rather than building from scratch.
Review and validate the outputs of more junior scientists, providing expert-level technical feedback and catching issues before they reach production.
Shape the direction of projects within the team, bringing experience and a point of view to decisions about approach, tooling, and scope.
Propose new techniques and methods where current approaches have clear limitations, and take responsibility for evaluating and introducing them.
Mentor peers and support the growth of colleagues at earlier career stages, through code reviews, pairing, and direct feedback.
Maintain thorough documentation and uphold the technical health of deliverables over time, not just at point of delivery.

Requirements

What you’ll need

Strong applied experience in machine learning and deep learning, with a track record of delivering solutions that have gone into production and affected real outcomes.
Effective proficiency in Python and core ML frameworks (e.g. PyTorch, TensorFlow, Hugging Face, scikit-learn), including model evaluation, experimentation, and iteration at scale.
Experience working directly with non-technical or semi-technical stakeholders to understand problems, frame requirements, and align on the right technical approach.
Ability to assess an existing capability or component critically and make a reasoned case for whether it fits a new context.
Comfortable shaping project direction and making significant technical decisions with appropriate autonomy.
Experience reviewing others' work at a high level of rigour, and providing feedback that improves both the output and the person who produced it.

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Pearson VUE Website LinkedIn All Job Openings 1001 - 5000 employees Founded 1994 📚 Education 🛍️ eCommerce ☁️ SaaS Education
eCommerce
SaaS 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. Advanced Specialist, AI Scientist Job not on LinkedIn 🔥 30 minutes ago 🇵🇱 Poland – Remote ⏰ Full Time 🟡 Mid-level 🟠 Senior 🧠 AI Research Scientist Python PyTorch Scikit-Learn Tensorflow Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Deliver robust, scalable ML solutions for AI capabilities in educational products, taking significant implementation decisions independently.
Work closely with Business Unit partners to understand their AI-related problems, help frame them clearly, and develop solutions that fit their actual needs.
Identify opportunities to reuse existing AI components and capabilities across teams. This includes use case discovery, technical solutioning, and making a credible case to partner teams for why reuse is the right path rather than building from scratch.
Review and validate the outputs of more junior scientists, providing expert-level technical feedback and catching issues before they reach production.
Shape the direction of projects within the team, bringing experience and a point of view to decisions about approach, tooling, and scope.
Propose new techniques and methods where current approaches have clear limitations, and take responsibility for evaluating and introducing them.
Mentor peers and support the growth of colleagues at earlier career stages, through code reviews, pairing, and direct feedback.
Maintain thorough documentation and uphold the technical health of deliverables over time, not just at point of delivery. 🎯 Requirements
Strong applied experience in machine learning and deep learning, with a track record of delivering solutions that have gone into production and affected real outcomes.
Effective proficiency in Python and core ML frameworks (e.g. PyTorch, TensorFlow, Hugging Face, scikit-learn), including model evaluation, experimentation, and iteration at scale.
Experience working directly with non-technical or semi-technical stakeholders to understand problems, frame requirements, and align on the right technical approach.
Ability to assess an existing capability or component critically and make a reasoned case for whether it fits a new context.
Comfortable shaping project direction and making significant technical decisions with appropriate autonomy.
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