AI Research Engineer – Multi-Modal Reinforcement Learning

Tether.to

. Conduct research on reinforcement learning algorithms for multimodal models, .

Posted 5/19/2026full-timeRemote • 🇮🇹 ItalyMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Conduct research on reinforcement learning algorithms for multimodal models,
Design and build reinforcement learning infrastructure that supports scalable training,
Develop and refine reward modeling strategies,
Create and curate multimodal simulation environments and datasets,
Analyze and optimize policy performance across modalities,
Investigate and develop next-generation reinforcement learning paradigms,
Publish research findings in top-tier conferences.

Requirements

What you’ll need

A Master's degree in Computer Science or a related field is required;
A PhD in Machine Learning, NLP, Computer Vision, or a closely related discipline is preferred,
Proven experience running large-scale reinforcement learning experiments in multimodal and vision-centric systems,
Deep understanding of reinforcement learning algorithms,
Strong proficiency in PyTorch and deep learning frameworks for vision and multimodal AI,
Proven track record of research publications in top-tier conferences.

Benefits

Comp & perks

Competitive salary
Flexible work arrangements
Professional development opportunities

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 & Tools

reinforcement learning algorithmsreward modeling strategiespolicy performance optimizationmultimodal modelslarge-scale reinforcement learning experimentsdeep learning frameworksPyTorchsimulation environmentsdatasetsnext-generation reinforcement learning paradigms

Certifications

Master's degree in Computer SciencePhD in Machine LearningPhD in NLPPhD in Computer Vision